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Glioma refers to a highly prevalent type of brain tumor that is strongly associated with a high mortality rate. During the treatment process of the disease, it is particularly important to accurately perform segmentation of the glioma from Magnetic Resonance Imaging (MRI). However, existing methods used for glioma segmentation usually rely solely on either local or global features and perform poorly in terms of capturing and exploiting critical information from tumor volume features. Herein, we propose a local and global dual transformer with an attentional supervision U-shape network called DTASUnet, which is purposed for glioma segmentation. First, we built a pyramid hierarchical encoder based on 3D shift local and global transformers to effectively extract the features and relationships of different tumor regions. We also designed a 3D channel and spatial attention supervision module to guide the network, allowing it to capture key information in volumetric features more accurately during the training process. In the BraTS 2018 validation set, the average Dice scores of DTASUnet for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) regions were 0.845, 0.905, and 0.808, respectively. These results demonstrate that DTASUnet has utility in assisting clinicians with determining the location of gliomas to facilitate more efficient and accurate brain surgery and diagnosis., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Stiffness-related eye diseases such as keratoconus require comprehensive visualization of the complex morphological matrix changes. The aim of this study was to use three-dimensional (3D) light sheet fluorescence microscopy (LSFM) to analyze unlabeled corneal tissue samples, qualitatively visualizing changes in corneal stiffness. Isolated porcine corneal tissue samples were treated with either NaCl or 0.1% glutaraldehyde (GTA) prior to clearing with benzyl alcohol/benzyl benzoate (BABB) and subsequently scanned with LSFM. After analysis of the LSFM data sets, the samples were embedded in paraffin to validate the results by conventional planar microscopy. In the unlabeled corneal tissue samples the 2D/3D morphology of the entire tissue volume was identified by specific autofluorescence signals. An enhancement of collagen crosslinking was induced by applying GTA to the corneal tissue. Subsequent LSFM scans showed specific morphological changes due to altered autofluorescence signals of the corneal stroma, which were confirmed by conventional histology. Therefore, LSFM analysis of corneal tissue samples allowed label-free 3D autofluorescence assessment of the corneal morphology in its anatomical context. It provides the technical basis for the examination of the pathologically altered cornea and facilitates ophthalmologic examinations of corneal diseases based on the altered tissue stiffness., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

In circumstances where antemortem information concerning the deceased individual is unavailable, forensic experts prepare biological profiling for unidentified human remains that aids in narrowing the search for identity. Biological profiling includes basic demographic information such as sex, age, stature, and ethnicity. Sex identification is the first and key step in the biological profiling of unidentified human remains, as it effectively reduces potential matches by excluding nearly one-half of the suspected cases and facilitates the subsequent stages. This study was conducted to assess the accuracy of artificial intelligence (AI) in predicting sex by analysing mandibular canine dimensions, mandibular intercanine distance (MICD), and mandibular canine index (MCI) obtained from three-dimensional (3D) digital impressions captured by using an intraoral scanner (IOS). The results of the receiver operating characteristic (ROC) test indicated that mean mandibular canine width (MeanMCW) had the highest sexual dimorphism with the area under the curve (AUC) of 0.912, and the Gaussian Naive Bayes (GNB) classifier demonstrated the highest testing accuracy among all machine learning (ML) models, achieving an accuracy of 92.5%. While the outcomes of this study are promising, further studies are imperative to validate these findings with larger sample sizes in different ethnic populations., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

The orbital bone presents a closed curve, and fracture results in disfigurement. An image analysis procedure was developed to examine before and after corrective surgery. An ellipse and circumscribed contour embodied the closed curve. Three-dimensional (3D) computed tomography (CT) images were collected from 25 patients. Orbital rim data were generated, and binary images were created to facilitate closed curve analysis. Various indices, including the solidity value (closed curve area/convex hull area) and ellipse distance (discrepancy between the closed curve and the ellipse traversing the curve), were utilized. The ratios of various indices-including the number of vertices, solidity value, and ellipse distance-between the affected and unaffected sides showed postoperative values that were closer to 1, which would indicate perfect symmetry, than the preoperative measurements (P < 0.05). The solidity value increased, while both the ellipse distance and curvature values decreased, reflecting the transformation of bends into smooth contours following reduction surgery (P < 0.05). Significant correlations were observed between 1-solidity, ellipse distance, and curvature using the Pearson correlation test (P < 0.05). This study validated postoperative changes in various indices and established correlations among multiple values, specifically solidity, ellipse distance, and curvature. Employing multiple indices with mutual complements has provided objective information confidently., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Early detection and accurate diagnosis of brain morphological abnormalities are essential for the effective management and treatment of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural magnetic resonance imaging (MRI) is a powerful support tool to aid in disease diagnosis and prediction. In this research study, we present an innovative approach to predict Alzheimer's disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework. The proposed method involves dividing the brain into 138 predetermined sections based on anatomical information. Next, we apply three-dimensional vision transformers (3D-ViTs) to each ROI individually, harnessing the power of deep learning. To improve prediction accuracy, we employ a deep belief network (DBN) as an ensemble learning model. Evaluating our approach on the baseline structural MRI dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and comparing it against five other competing models, we demonstrate its performance across four binary classification tasks and a three-class classification test (AD vs MCI vs CN (Cognitively Normal)). The proposed system outperforms existing models and provides interpretable insights into the brain regions that significantly contribute to solving each classification problem. Our findings align with the existing body of literature and hold promise for guiding future research directions in this domain., Competing Interests: Declarations Competing interests The authors declare no competing interests, (© 2024. The Author(s).)

Cervical pedicle screws (CPS) provide biomechanically superior fixation compared to other techniques but are technically more demanding. Navigated CPS placement has been increasingly reported as a safe and accurate technique, yet there are few studies comparing different combinations of imaging and navigation systems under comparable conditions. With this study, we aimed to compare different imaging and navigation systems for CPS placement in terms of accuracy, screw placement time and applied radiation dose. For this experimental study, navigated CPS placement was performed at levels C2 to C7 in 24 identical radiopaque artificial spine models by two surgeons with different levels of experience using three different combinations of intraoperative 3D imaging devices and navigation systems. Accuracy, time and radiation dose were compared between the groups. In total, 288 screws were placed. Accuracy was > 98% in all groups with no significant differences between groups or between surgeons (P = 0.30 and P = 0.31, respectively), but the inexperienced surgeon required significantly more time (P < 0.001). Radiation dose was significantly higher with iCT compared to CBCT (P < 0.0001). Under experimental conditions, accuracy rates of > 98% were achieved for navigated CPS placement regardless of the imaging modality or navigation system used. Radiation doses were significantly lower for CBCT compared to iCT guidance., Competing Interests: Declarations Competing interests The research group MINTOS had grants/grants pending and technical support from Siemens Healthineers (Erlangen, Germany) and Nuvasive Inc. (San Diego, California, USA). The funders were not involved in the study conceptualization, design, data collection, analysis, decision to publish or the preparation of the manuscript. PAG serves as unpaid member of a consulting/advisory board for Siemens Healthineers. The other authors declare that they have no financial or non-financial interests to disclose. IRB approval Not applicable due to the experimental character of the study., (© 2024. The Author(s).)

In patients with anterior shoulder instability (ASI), a chronic imbalance might exist between the anterior and posterior shoulder muscles (i.e., subscapular [Ssc] vs. infraspinatus and teres minor [Isp + TM]). The balance could be evaluated as the ratio of volume (VR Ssc/Isp+TM ) and computed tomography density (CT-DR Ssc/Isp+TM ) using a 3D sectional approach. A total of 28 CT images (19 patients) of non-pathological shoulders (clavicular fracture [CF]) and 17 CT images (17 patients) of ASI were used. Segmentation of Ssc and Isp + TM muscles was performed; the reconstructed models were separated by the Y-view plane and the planes situated 2.5 cm and 5 cm medial to the Y-view plane to generate muscle models. VR Ssc/Isp+TM and CT-DR Ssc/Isp+TM were measured in each section of both the groups, as well as in whole muscle measurements in the CF group. VR Ssc/Isp+TM and CT-DR Ssc/Isp+TM obtained through a 3D sectional approach in the CF group were comparable to the whole muscle measurements, with a value of 1.06 for both. In the ASI group, the VR Ssc/Isp+TM was higher, whereas the CT-DR Ssc/Isp+TM was lower than those in the CF group. The VR Ssc/Isp+TM and CT-DR Ssc/Isp+TM appeared to be balanced in the CF group. However an imbalance was observed in the ASI group. Our 3D sectional measurement approach has the potential to assess the balance between the anterior and posterior shoulder muscles in ASI., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

We demonstrate a new tomographic imaging device for 2D cross-sectional and 3D volumetric images of the anterior eye segment. The optical system combines optical sectioning with a stereoscopic camera setup to determine the true spatial location of the ocular structures. Moreover, we propose a calibration procedure to estimate the triangulation function that links the stereo camera pixel indices with the eye-referenced 3D spatial coordinates. The proposed instrument will image the entire anterior chamber and contiguous structures, including the corneal limbus, sclera and eyelids. Furthermore, the system can obtain corneal topography. Finally, we studied the repeatability of the instrument and its agreement with a commercial Scheimpflug imaging tomographer in a group of healthy subjects., Competing Interests: Declarations Competing interests AJV, DH and SC are inventors and applicants of the provisional Patent 63/644,464, which describes the methodology for 3D tomographic reconstruction. AM, TKR and DK declare no potential conflict of interest., (© 2024. The Author(s).)

Estimation of cochlear length is gaining attention in the field of cochlear implants (CIs), mainly for selecting of CI electrode lengths. The currently available tools to estimate the cochlear duct length (CDL) are only valid for normal inner anatomy. However, inner ear malformation (IEM) types are associated with different degrees of cystic apices, limiting the application of CDL equations of normal anatomy inner ear. Therefore, this study aimed to understand the degree to which the outer wall (OW) is observed in different malformation types and to formulate mathematical equations to estimate the OW length (OWL) from cochlear parameters, namely the basal turn diameter (A-value) and width (B-value). Three-dimensional (3D) segmentation of promontory and fluid parts of the inner ear was performed to understand the extent to which the OW is visible to measure the OWL manually. Enlarged vestibular aqueduct syndrome (EVAS) was diagnosed in 37 ears, which consistently showed the extent of the OW to an angular depth of 540°, beyond which the cystic apex starts. Incomplete partition (IP) type I was observed in 30 ears, with the OW extending to only 360° of angular depth. IP type II was observed in 35 ears, with the OW extending to 450° of angular depth. IP type III was identified in 24 ears, with the OW observed for 540° of angular depth. Cavity-type malformations were observed in 36 ears, and circumference was measured in the axial view. A strong positive linear correlation was observed between the manually measured OWL and cochlear parameters for all malformation types analyzed. A multiple linear regression model was applied to formulate mathematical equations, which was further used to create a software application for estimating OWLs in IEM types, using cochlear parameters as inputs., (© 2024. The Author(s).)

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using HiP-CT as part of the Human Organ Atlas Project. HiP-CT pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering a resolution of around 20 μm/voxel and enabling highly detailed localised zooms up to 1-2 μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice similarity coefficient (DSC) scores of 0.9523, 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline DSC ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydrostatic pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Our study establishes the benchmark across various evaluation metrics, for vascular segmentation of HiP-CT imaging data, an imaging technology that has the potential to substantively shift our understanding of human vascular networks., (© 2024. The Author(s).)

Extended reality (XR) technologies are emerging as promising platforms for medical training and procedural guidance, particularly in complex cardiac interventions. This paper presents a high-fidelity methodology to perform real-time 3D catheter tracking and visualization during simulated cardiac interventions. A custom 3D-printed setup with mounted cameras enables biplane video capture of a catheter. A computer vision algorithm processes the biplane images in real-time to reconstruct the 3D catheter trajectory represented by any designated number of points along its length. This method accurately localizes the catheter tip within 1 mm and can reconstruct any arbitrary catheter configuration. The tracked catheter data is integrated into an interactive Unity-based scene rendered on the Meta Quest 3 headset. The visualization seamlessly combines a reconstructed 3D patient-specific heart model with the dynamically tracked catheter, creating an immersive extended reality training environment. Our experimental study, involving six participants, demonstrated that the 3D visualization provided by the proposed XR system significantly outperformed 2D visualization in terms of speed and user experience. This suggests that the XR system has the potential to enhance catheterization training by improving spatial comprehension and procedural skills. The proposed system demonstrates the potential of XR technologies to transform percutaneous cardiac interventions through improved visualization and interactivity., (© 2024. The Author(s).)

Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate dental prosthetic processes. Tooth images are required to train deep learning models, but they are difficult to use in research because they contain personal patient information. Therefore, we propose a method for generating virtual tooth images using image-to-image translation (pix2pix) and contextual reconstruction fill (CR-Fill). Various virtual images can be generated using pix2pix, and the images are used as training images for CR-Fill to compare the real image with the virtual image to ensure that the teeth are well-shaped and meaningful. The experimental results demonstrate that the images generated by the proposed method are similar to actual images. In addition, only using virtual images as training data did not perform well; however, using both real and virtual images as training data yielded nearly identical results to using only real images as training data., (© 2024. The Author(s).)

Knee osteoarthritis is characterized by articular cartilage wear, with its morphological progression not fully understood. This study aimed to elucidate factors contributing to femoral cartilage defects and their expansion in medial knee osteoarthritis, using a novel approach analyzing cross-sectional MRI data arranged by disease severity. From a cohort of 277 women in the Kanagawa Knee Study, we selected 17 knees that showed a cartilage area ratio < 0.99 in the posteromedial femoral cartilage region as the subjects for this study. The morphological relationships between femoral cartilage defects and menisci, as well as between femoral cartilage defects and tibial cartilage lesions, were investigated. Among subjects aged 30 to 79 years, the proportion was significantly higher in the 70-79 age group. In 11 cases, the outer edge of the cartilage defect was observed to coincide with the inner edge of the medial meniscus. Tibial cartilage lesions corresponded to femoral cartilage defects in 15 cases. Our 3D MRI analysis demonstrated that femoral cartilage defects were initially caused by either medial meniscus extrusion or kissing tibial cartilage lesions, with subsequent expansion of these defects resulting from the combined effects of ongoing medial meniscus extrusion and progressive tibial cartilage degeneration.Trial registration: UMIN, UMIN000032826; September 1, 2018., (© 2024. The Author(s).)

Deep learning-based image analysis offers great potential in clinical practice. However, it faces mainly two challenges: scarcity of large-scale annotated clinical data for training and susceptibility to adversarial data in inference. As an example, an artificial intelligence (AI) system could check patient positioning, by segmenting and evaluating relative positions of anatomical structures in medical images. Nevertheless, data to train such AI system might be highly imbalanced with mostly well-positioned images being available. Thus, we propose the use of synthetic X-ray images and annotation masks forward projected from 3D photon-counting CT volumes to create realistic non-optimally positioned X-ray images for training. An open-source model (TotalSegmentator) was used to annotate the clavicles in 3D CT volumes. We evaluated model robustness with respect to the internal (simulated) patient rotation α on real-data-trained models and real&synthetic-data-trained models. Our results showed that real&synthetic- data-trained models have Dice score percentage improvements of 3% to 15% across different α groups compared to the real-data-trained model. Therefore, we demonstrated that synthetic data could be supplementary used to train and enrich heavily underrepresented conditions to increase model robustness., (© 2024. The Author(s).)

Chemical exchange saturation transfer (CEST) imaging may provide novel contrast for the diagnosis, prognosis, and monitoring of the progression or treatment of neurological applications. However, the reproducibility of prominent CEST contrasts like amide CEST and nuclear Overhauser enhancement (NOE) CEST must be characterized in healthy brain gray matter (GM) and white matter (GM) prior to clinical implementation. The objective of this study was to characterize the reproducibility of four different CEST contrasts in the healthy human brain. Using a 3T MRI scanner, two 3D CEST scans were acquired in 12 healthy subjects (7 females, mean age (± SD) 26 ± 4 years) approximately 10 days apart. Scan-rescan reproducibility was measured for four contrasts: amine/amide concentration-independent detection (AACID), Amide*, and inverse magnetization transfer ratio (MTR Rex ) contrast for amide and NOE. Reproducibility was evaluated between- and within-subjects using coefficients of variation (CV) and the percent difference between measurements. AACID and NOE-MTR Rex contrasts demonstrated the lowest within-subject CVs (0.8-1.2% and 1.6-2.0%, respectively), between-subject CVs (1.2-2.1% and 3.4-4.2%, respectively), and percent difference (1.2-1.4% and 2.2-2.8%, respectively) for both GM and WM. AACID and NOE-MTR Rex contrasts demonstrated the highest reproducibility and represented stable measurements suitable for characterizing changes in brain tissue caused by pathological processes., (© 2024. The Author(s).)

Due to the development of magnetic resonance (MR) imaging processing technology, image-based identification of endolymphatic hydrops (EH) has played an important role in understanding inner ear illnesses, such as Meniere's disease or fluctuating sensorineural hearing loss. We segmented the inner ear, consisting of the cochlea, vestibule, and semicircular canals, using a 3D-based deep neural network model for accurate and automated EH volume ratio calculations. We built a dataset of MR cisternography (MRC) and HYDROPS-Mi2 stacks labeled with the segmentation of the perilymph fluid space and endolymph fluid space of the inner ear to devise a 3D segmentation deep neural network model. End-to-end learning was used to segment the perilymph fluid and the endolymph fluid spaces simultaneously using aligned pair data of the MRC and HYDROPS-Mi2 stacks. Consequently, the segmentation performance of the total fluid space and endolymph fluid space had Dice similarity coefficients of 0.9574 and 0.9186, respectively. In addition, the EH volume ratio calculated by experienced otologists and the EH volume ratio value predicted by the proposed deep learning model showed high agreement according to the interclass correlation coefficient (ICC) and Bland-Altman plot analysis., (© 2024. The Author(s).)

Accurate sex estimation is crucial for comprehensive analysis of the biological profiles of unidentified human skeletal remains. However, there is a notable lack of research specifically addressing the morphometrics of the hard palate. Therefore, this study aimed to derive discriminant equations using the hard palate and assess their applicability for sexing partial skeletal remains in a contemporary Korean population. Statistical analyses were performed for 24 measurements derived from three-dimensional models of the hard palate, generated using computed tomography scans of 301 individuals (156 males, 145 females). Descriptive statistics revealed significant sexual dimorphism in the mean comparison of hard palate sizes between Korean males and females, with males exhibiting larger palates across all measurements (p < 0.05). Discriminant function score equations were generated to aid in sex determination. Univariate analysis yielded an accuracy range of 57.8-75.1%, whereas the stepwise method achieved an accuracy of 80.7% with five selected variables: IF-PNS, GFL-GFR, IF-GFR, Pr-EcL, and Pr-EnR. The results of this metric analysis demonstrate the usefulness of the hard palate for sex estimation in the contemporary Korean population. These findings have potential implications for forensic investigations, archeological studies, and population-specific anatomical research., (© 2024. The Author(s).)

The study of biological soft tissue structures at the micron scale details the function of healthy and pathological tissues, which is vital in the diagnosis and treatment of diseases. Speckle based X-ray phase contrast tomographic scans at a nanometer scale have the potential to thoroughly analyse such tissues in a quantitative and qualitative manner. Diamond light source, the UKs national synchrotron facility developed and refined a 1-D X-ray speckle-based imaging technique, referred to as Fly scan mode. This novel image acquisition technique was used to perform a rapid structural composition scan of rodent lung histology samples. The rodent samples were taken from healthy and Staphylococcus aureus induced acute respiratory distress syndrome models. The analysis and cross comparison of the fly scan method, absorption-based tomography and conventional histopathology H&E staining microscopy are discussed in this paper. This analysis and cross comparison outline the ways the speckle-based technique can be of benefit. These advantages include improved soft tissue contrast, 3-D volumetric rendering, segmentation of specific gross tissue structures, quantitative analysis of gross tissue volume. A further advantage is the analysis of cellular distribution throughout the volumetric rendering of the tissue sample. The study also details the current limitations of this technique and points to ways in which future work on this imaging modality may progress., (© 2024. The Author(s).)

Collective cancer invasion exhibits a hierarchical structure characterized by leader-follower organization. Dynamic gene expression analysis of invading cells using nanobiosensors within 3D microenvironments provides a valuable means to explore the regulation of leader cells during collective cancer invasion. Nonetheless, the analysis of time-lapse, multimodal images that capture the intricacies of complex invading structures and gene expression profiles in 3D tumor spheroids poses a significant technological challenge. Here, we present a computer vision-based workflow that streamlines the identification of protrusions and detached clusters from 3D tumor spheroids. This methodology not only discerns invading multicellular structures and quantifies their physical properties, but also captures gene expression patterns associated with these invasive mechanisms using an intracellular nanobiosensor. Consequently, it empowers a systematic exploration of the genotypic and phenotypic heterogeneities inherent in cancer invasion. To illustrate the effectiveness of this approach, we applied it to the analysis of a long noncoding RNA, MALAT1, in tumor spheroids derived from patients with muscle-invasive bladder cancer. Our investigation delved into the heterogeneity of cancer invasion and its relationship to MALAT1 expression. Overall, this workflow represents a valuable tool for gaining insights into the complexities of cancer invasion., (© 2024. The Author(s).)

Parkinson's disease (PD) is one of the most common neurodegenerative disorders that affect the quality of human life of millions of people throughout the world. The probability of getting affected by this disease increases with age, and it is common among the elderly population. Early detection can help in initiating medications at an earlier stage. It can significantly slow down the progression of this disease, assisting the patient to maintain a good quality of life for a more extended period. Magnetic resonance imaging (MRI)-based brain imaging is an area of active research that is used to diagnose PD disease early and to understand the key biomarkers. The prior research investigations using MRI data mainly focus on volume, structural, and morphological changes in the basal ganglia (BG) region for diagnosing PD. Recently, researchers have emphasized the significance of studying other areas of the human brain for a more comprehensive understanding of PD and also to analyze changes happening in brain tissue. Thus, to perform accurate diagnosis and treatment planning for early identification of PD, this work focuses on learning the onset of PD from images taken from whole-brain MRI using a novel 3D-convolutional neural network (3D-CNN) deep learning architecture. The conventional 3D-Resent deep learning model, after various hyper-parameter tuning and architectural changes, has achieved an accuracy of 90%. In this work, a novel 3D-CNN architecture was developed, and after several ablation studies, the model yielded results with an improved accuracy of 93.4%. Combining features from the 3D-CNN and 3D ResNet models using Canonical Correlation Analysis (CCA) resulted in 95% accuracy. For further enhancements of the model performance, feature fusion with optimization was employed, utilizing various optimization techniques. Whale optimization based on a biologically inspired approach was selected on the basis of a convergence diagram. The performance of this approach is compared to other methods and has given an accuracy of 97%. This work represents a critical advancement in improving PD diagnosis techniques and emphasizing the importance of deep nested 3D learning and bio-inspired feature selection., (© 2024. The Author(s).)

Craniofacial structure and dental hard tissue used to be researched on by traditional imaging tools such as light microscope, electron microscope and micro-CT. Due to the limitations of imaging principle, resolution and 3D rendering reconstruction technique, traditional imaging tools are constrained for presenting fine structure and precise measurements. Here a brand-new imaging equipment-3D X-ray microscope is introduced to realize a more efficient scanning by demonstrating the comparison of the craniofacial structures and dental hard tissue of diabetes and normal DBA mouse. To explore a higher resolution, more efficient imaging measurement and 3D reconstruction method on craniofacial structure and dental hard tissue. The study included 12 DBA mice which were divided into two groups (control group and diabetes group). The heads were separated and scanned by 3D X-ray microscope, after which regions of interest were selected, followed by measurement and 3D reconstruction based on microscope attached software Dragonfly pro©. Hemi-mandibles were collected for enamel mineral density assessment supported by QRM-MicroCT-HA phantom. Data was submitted to paired t-tests at a 95% confidence level. The automatic assessed enamel thickness of diabetes mice decreased on average, whereas the rest of manual measurements and automatic assessed density showed no statistical difference. We constructed HA phantom assisted enamel density procedure in Dragonfly software. Craniofacial structure and dental hard tissue were well-presented both in 2D slide and 3D reconstruction viewport by 3D X-ray microscope which can be routinely used as craniofacial structure and dental hard tissue imaging tool., (© 2024. The Author(s).)

Pacific saury is one of the most economically important species in the northwestern Pacific Ocean. The management of this resource relies on precise input of biological data such as body length and is often hindered by a lack of such data on captured fish. This study explores the potential of electronic monitoring (EM) using off-the-shelf stereo cameras to overcome the challenges of collecting and measuring saury body length from the Pacific saury fishery. Using a calibrated WEEVIEW SID WV3000 3D camera, a total of 252 paired images with different shooting angles and distances were obtained for further measurement using Sebastes Stereo Image Analysis Software (SSIAS). The treatments for the measurement distance (MD) were 30 cm, 60 cm, and 100 cm, for the depression angle (DA) were 30°, 60°, and 90°, and for the position angle (PA) were 0°, 45°, and 315° (- 45°). An assistant calibration (AC) in the form of a 16 cm black ruler was also added used. The SSIAS measurement results indicated that the best measurement was obtained with 0° position angle, 90° depression angle, and 30 cm distance from the target fish. The use of AC in the SSIAS + AC measurement was proven to reduce the measurement error from 2.45 to 8.64% to - 1.86 to 0.01%. This study set the baseline for the application of EM on collecting saury body length and the use of AC has been proven to increase the measurement accuracy., (© 2024. The Author(s).)

Brain organoids provide a unique opportunity to model organ development in a system similar to human organogenesis in vivo. Brain organoids thus hold great promise for drug screening and disease modeling. Conventional approaches to organoid characterization predominantly rely on molecular analysis methods, which are expensive, time-consuming, labor-intensive, and involve the destruction of the valuable three-dimensional (3D) architecture of the organoids. This reliance on end-point assays makes it challenging to assess cellular and subcellular events occurring during organoid development in their 3D context. As a result, the long developmental processes are not monitored nor assessed. The ability to perform non-invasive assays is critical for longitudinally assessing features of organoid development during culture. In this paper, we demonstrate a label-free high-content imaging approach for observing changes in organoid morphology and structural changes occurring at the cellular and subcellular level. Enabled by microfluidic-based culture of 3D cell systems and a novel 3D quantitative phase imaging method, we demonstrate the ability to perform non-destructive high-resolution quantitative image analysis of the organoid. The highlighted results demonstrated in this paper provide a new approach to performing live, non-destructive monitoring of organoid systems during culture., (© 2024. The Author(s).)

Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT's diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan. By leveraging a transfer learning technique, we trained and validated our CAD framework on a unique dataset consisting of 3,831 DBT scans and subsequently tested it on 685 scans. Of the architectures tested, the Swin Transformer outperformed the ResNet101 and vanilla Vision Transformer. It achieved an impressive AUC score of 0.934 ± 0.026 at a resolution of 384 × 384. Increasing the image resolution from 224 to 384 not only maintained vital image attributes but also led to a marked improvement in performance (p-value = 0.0003). The Mean Teacher algorithm, a semi-supervised method using both labeled and unlabeled DBT slices, showed no significant improvement over the supervised approach. Comprehensive analyses across different lesion types, sizes, and patient ages revealed consistent performance. The integration of attention mechanisms yielded a visual narrative of the model's decision-making process that highlighted the prioritized regions during assessments. These findings should significantly propel the methodologies employed in DBT image analysis by setting a new benchmark for breast cancer diagnostic precision., (© 2024. The Author(s).)

Three-dimensional correlative multimodal and multiscale imaging is an emerging method for investigating the complex hierarchical structure of biological materials such as bone. This approach synthesizes images acquired across multiple length scales, for the same region of interest, to provide a comprehensive view of the material structure of a sample. Here, we develop a workflow for the structural analysis of human trabecular bone using a femtosecond laser to produce a precise grid to facilitate correlation between imaging modalities and identification of structures of interest, in this case, a single trabecula within a volume of trabecular bone. Through such image registration, high resolution X-ray microscopy imaging revealed fine architectural details, including the cement sheath and bone cell lacunae of the selected bone trabecula. The selected bone volume was exposed with a combination of manual polishing and site-specific femtosecond laser ablation and then examined with plasma focused ion beam-scanning electron microscopy. This reliable and versatile correlation approach has the potential to be applied to a variety of biological tissues and traditional engineered materials. The proposed workflow has the enhanced capability for generating highly resolved and broadly contextualized structural data for a better understanding of the architectural features of a material spanning its macroscopic to nanoscopic levels., (© 2024. The Author(s).)

In the field of spinal pathology, sagittal balance of the spine is usually judged by the spatial structure and morphology of pelvis, which can be represented by pelvic parameters. Pelvic parameters, including pelvic incidence, pelvic tilt and sacral slope, are therefore essential for the diagnosis and treatment of spinal disorders, however, it is a time-consuming and laborious procedure to measure these parameters by traditional methods. In this paper, an automatic measurement framework for pelvic CT images was proposed to calculate three-dimensional (3D) pelvic parameters with the support of deep learning technology. Pelvic images were first preprocessed, and 3D reconstruction was then performed to obtain 3D pelvic model by the Visualization Toolkit. DRINet was trained to segment the femoral head region in the pelvic images, and 3D sphere fitting was performed to locate the femoral heads. In addition, VGG16 was adopted to recognize images containing superior sacral endplate, and the plane growth algorithm was used to fit the plane so that the midpoint and normal vector of the superior sacral endplate could be obtained. Finally, 3D pelvic parameters were automatically calculated, and compared with manual measurements for 15 patients. The proposed framework automatically generated 3D pelvic models, and calculated two-dimensional (2D) and 3D pelvic parameters from continuous CT images. Experiments demonstrated that the framework can greatly speed up the calculation of pelvic parameters, and these parameters are accurate when compared with the manual measurements. In conclusion, the proposed framework demonstrates good performance on automatic pelvimetry measurement by incorporating deep learning technology, and can well replace the traditional methods for pelvic parameter measurement., (© 2024. The Author(s).)

Data augmentation is a technique usually deployed to mitigate the possible performance limitation from training a neural network model on a limited dataset, especially in the medical domain. This paper presents a study on effects of applying different rotation settings to augment cardiac volumes from the Multi-modality Whole Heart Segmentation dataset, in order to improve the segmentation performance. This study presents a comparison between conventional 2D (slice-wise) rotation primarily on the axial axis, 3D (volume-wise) rotation, and our proposed rotation setting that takes into account possible cardiac alignment according to its anatomy. The study has suggested two key considerations: 2D slice-wise rotation should be avoided when using 3D data for segmentation, due to intrinsic structural correlation between subsequent slices, and that 3D rotations may help improve segmentation performance on data previously unseen to the model., (© 2024. The Author(s).)

Recent developments in Deep Learning have opened the possibility for automated segmentation of large and highly detailed CT scan datasets of fossil material. However, previous methodologies have required large amounts of training data to reliably extract complex skeletal structures. Here we present a method for automated Deep Learning segmentation to obtain high-fidelity 3D models of fossils digitally extracted from the surrounding rock, training the model with less than 1%-2% of the total CT dataset. This workflow has the capacity to revolutionise the use of Deep Learning to significantly reduce the processing time of such data and boost the availability of segmented CT-scanned fossil material for future research outputs. Our final Unet segmentation model achieved a validation Dice similarity of 0.96., (© 2024. Crown.)

Simultaneous imaging of the SPECT tracer 131 I and PET tracer 18 F is important in the diagnosis of high- and low-grade thyroid cancers because high-grade thyroid cancers have high 18 F-FDG and low 131 I uptake, while low-grade thyroid cancers have high 131 I and low 18 F-FDG uptake. In this study, Na 131 I and 18 F-FDG were simultaneously imaged using the Compton-PET system, in vivo. The angular resolution and sensitivity of the Compton camera with 356 keV gamma ray measured using a 133 Ba point source were 12.3° and 2 × 10 -5 , respectively. The spatial resolution and sensitivity of PET were measured with a 22 Na point source. The transaxial and axial spatial resolutions of the PET at the center of the FOV were 1.15 mm and 2.04 mm, respectively. Its sensitivity was 1.2 × 10 -4 . In-vivo images of the 18 F and 131 I isotopes were simultaneously acquired from mice. These showed that 18 F-FDG was active in the heart, brown fat, and brain, while Na 131 I was active in the thyroid, stomach, and bladder. Artifacts were found in the Compton camera images when the activity of 131 I was much lower than that of 18 F. This study demonstrates the potential of simultaneous clinical imaging of 18 F and 131 I., (© 2024. The Author(s).)

The structural architecture of coral reefs is a known predictor of species richness, fish biomass and reef resilience. At a smaller scale, three-dimensional (3D) surface area of corals is a fundamental determinant of physical and biological processes. Quantifying the 3D surface area of corals has applications for a broad range of scientific disciplines, including carbonate production estimates, coral predation studies, and assessments of reef growth. Here, we present morphotaxon-specific conversion metrics to estimate total 3D surface area and projected 2D surface area of individual colonies from simple field measurements of colony maximum diameter. Underwater photogrammetry techniques were used to quantify surface area and estimate conversion metrics. Bayesian models showed strong non-linear (power) relationships between colony maximum diameter and both total 3D surface area and projected 2D surface area for 13 out of 15 morphotaxa. This study presents a highly resolved and efficient method for obtaining critical surface area assessments of corals for various applications, including assessments of biotic surface area, tissue biomass, calcification rates, coral demographic rates, and reef restoration monitoring., (© 2024. The Author(s).)

To evaluate the repeatability of a novel automated technique called Smart ERA (Smart Endometrial Receptivity Analysis) for the automated segmentation and volume calculation of the endometrium in patients with normal uteri,, and to compare the agreement of endometrial volume measurements between Smart ERA, the semi-automated Virtual Organ Computer-aided Analysis (VOCAL) technique and manual segmentation. This retrospective study evaluated endometrial volume measurement in infertile patients who underwent frozen-thawed embryo transfer (FET). Transvaginal three-dimensional ultrasound scans were performed using a Resona R9 ultrasound machine. Data was collected from patients between 2021 and 2022. Patients with normal uteri and optimal ultrasound images were included. Endometrial volumes were measured using Smart ERA, VOCAL at 15° rotation, and manual segmentation. Intra-observer repeatability and agreement between techniques were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. A total of 407 female patients were evaluated (mean age 33.2 ± 4.7 years). The repeatability of Smart ERA showed an ICC of 0.983 (95% CI 0.984-0.991). The agreement between Smart ERA and the manual method, Smart ERA and VOCAL, and VOCAL and the manual method, as assessed by ICC, were 0.986 (95% CI 0.977-0.990), 0.943 (95% CI 0.934-0.963), and 0.951 (95% CI 0.918-0.969), respectively. The Smart ERA technique required approximately 3 s for endometrial volume calculation, while VOCAL took around 5 min and the manual segmentation method took approximately 50 min. The Smart-ERA software, which employs a novel three-dimensional segmentation algorithm, demonstrated excellent intra-observer repeatability and high agreement with both VOCAL and manual segmentation for endometrial volume measurement in women with normal uteri. However, these findings should be interpreted with caution, as the algorithm's performance may not be generalizable to populations with different uterine characteristic. Additionally, Smart ERA required significantly less time compared to VOCAL and manual segmentation., (© 2024. The Author(s).)

This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including ureter and renal pelvis, on non-enhanced computed tomography (NECT) scans. A total of 150 NECT scans with normal appearance of the left UUT were chosen for this study. The dataset was divided into training (n = 130) and validation sets (n = 20). The test set contained 29 randomly chosen cases with computed tomography urography (CTU) and NECT scans, all with normal appearance of the left UUT. An experienced radiologist marked out the left renal pelvis and ureter on each scan. Two types of frameworks (entire and sectional) with three types of DL models (basic UNet, UNet3 + and ViT-UNet) were developed, and evaluated. The sectional framework with basic UNet model achieved the highest mean precision (85.5%) and mean recall (71.9%) on the test set compared to all other tested methods. Compared with CTU scans, this method had higher axial UUT recall than CTU (82.5% vs 69.1%, P < 0.01). This method achieved similar or better visualization of UUT than CTU in many cases, however, in some cases, it exhibited a non-ignorable false-positive rate. The proposed DL method demonstrates promising potential in automated 3D UUT segmentation on NECT scans. The proposed DL models could remarkably improve the efficiency of UUT reconstruction, and have the potential to save many patients from invasive examinations such as CTU. DL models could also serve as a valuable complement to CTU., (© 2024. The Author(s).)

Recent advances in light microscopy have enabled single molecules to be imaged and tracked within living cells and this approach is impacting our understanding of cell biology. Computer modeling and simulation are important adjuncts to the experimental cycle since they aid interpretation of experimental results and help refine, test and generate hypotheses. Object-oriented computer modeling is particularly well-suited for simulating random, thermal, movements of individual molecules as they interact with other molecules and subcellular structures, but current models are often limited to idealized systems consisting of unit volumes or planar surfaces. Here, a simulation tool is described that combines a 3-dimensional, voxelated, representation of the cell consisting of subcellular structures (e.g. nucleus, endoplasmic reticulum, cytoskeleton, vesicles, and filopodia) combined with numerical floating-point precision simulation of thousands of individual molecules moving and interacting within the 3-dimensional space. Simulations produce realistic time-series video sequences comprising single fluorophore intensities and realistic background noise which can be directly compared to experimental fluorescence video microscopy data sets., (© 2024. The Author(s).)

In this paper, we investigate the effectiveness of shape completion neural networks as clinical aids in maxillofacial surgery planning. We present a pipeline to apply shape completion networks to automatically reconstruct complete eumorphic 3D meshes starting from a partial input mesh, easily obtained from CT data routinely acquired for surgery planning. Most of the existing works introduced solutions to aid the design of implants for cranioplasty, i.e. all the defects are located in the neurocranium. In this work, we focus on reconstructing defects localized on both neurocranium and splanchnocranium. To this end, we introduce a new dataset, specifically designed for this task, derived from publicly available CT scans and subjected to a comprehensive pre-processing procedure. All the scans in the dataset have been manually cleaned and aligned to a common reference system. In addition, we devised a pre-processing stage to automatically extract point clouds from the scans and enrich them with virtual defects. We experimentally compare several state-of-the-art point cloud completion networks and identify the two most promising models. Finally, expert surgeons evaluated the best-performing network on a clinical case. Our results show how casting the creation of personalized implants as a problem of shape completion is a promising approach for automatizing this complex task., (© 2024. The Author(s).)

The automatic segmentation of the pharyngeal airway space has many potential medical use, one of which is to help facilitate the creation of the Tubingen Palatal Plate. Therefore, it is of great importance to understand which methods are suitable for this task. Here, neural network based solutions available in the literature are compared to find the best methods. Neural network models were chosen to encompass a diverse landscape. Some models were taken from the general semantic segmentation literature, while others were taken from the medical or pharyngeal airway space segmentation literature. Some models are convolutional neural networks, while others are transformer-based model or a mix of both convolutional and transformer-based model. These models include 2d/3d U-Net, Deeplabv3, YOLOv8, Swinv2 UNETR, SegFormer, and 3D MRU-Net. Furthermore, additional strategies to enhance performance were also considered. These strategies consisted of training two separate networks in multiple stages as well leveraging unlabeled data to pretrain the neural networks before fine-tuning them on the labeled data. It was found that out of all the models considered here, the 2d U-Net performed the best achieving an average dice score of 0.9180 ± 0.0111. Out of all the strategies to enhance performance, only two strategies improve the actual results but only by a small margin. Therefore, these strategies can be consider if a small increase in performance is desired from the 2d U-Net at the expense of computational resource., (© 2024. The Author(s).)

Developing advanced systems for 3D brain tissue segmentation from neonatal magnetic resonance (MR) images is vital for newborn structural analysis. However, automatic segmentation of neonatal brain tissues is challenging due to smaller head size and inverted T1/T2 tissue contrast compared to adults. In this work, a subject-specific atlas based technique is presented for segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from neonatal MR images. It involves atlas selection, subject-specific atlas creation using random forest (RF) classifier, and brain tissue segmentation using the expectation maximization-Markov random field (EM-MRF) method. To increase the segmentation accuracy, different tissue intensity- and gradient-based features were used. Evaluation on 40 neonatal MR images (gestational age of 37-44 weeks) demonstrated an overall accuracy of 94.3% and an average Dice similarity coefficient (DSC) of 0.945 (GM), 0.947 (WM), and 0.912 (CSF). Compared to multi-atlas segmentation methods like SEGMA and EM-MRF with multiple atlases, our method improved accuracy by up to 4%, particularly in complex tissue regions. Our proposed method allows accurate brain tissue segmentation, a crucial step in brain magnetic resonance imaging (MRI) applications including brain surface reconstruction and realistic head model creation in neonates., (© 2024. The Author(s).)

The importance of 3D reconstruction of coronary arteries using multiple coronary angiography (CAG) images has been increasingly recognized in the field of cardiovascular disease management. This process relies on the camera matrix's optimization, needing correspondence info for identical point positions across two images. Therefore, an automatic method for determining correspondence between two CAG images is highly desirable. Despite this need, there is a paucity of research focusing on image matching in the CAG images. Additionally, standard deep learning image matching techniques often degrade due to unique features and noise in CAG images. This study aims to fill this gap by applying a deep learning-based image matching method specifically tailored for the CAG images. We have improved the structure of our point detector and redesigned loss function to better handle sparse labeling and indistinct local features specific to CAG images. Our method include changes to training loss and introduction of a multi-head descriptor structure leading to an approximate 6% improvement. We anticipate that our work will provide valuable insights into adapting techniques from general domains to more specialized ones like medical imaging and serve as an improved benchmark for future endeavors in X-ray image-based correspondence matching., (© 2024. The Author(s).)

It has not yet been proven whether sepsis affects the tissue around the anal canal. To address this issue, we established three-dimensional models for various types of anorectal abscesses and utilize 3D reconstruction of Magnetic Resonance Imaging scans to assess the extent of muscle damage caused by anorectal abscesses. Patients diagnosed with anorectal abscess, selected from January 2019 to January 2022 underwent pre- and post-operative scanning of pelvic floor and perianal tissues. The aforementioned structures were segmented for the reconstruction of a three-dimensional visual model and measurement of volumes for the abscess as well as the internal and external sphincters and levator ani muscle. The study included a total of 42 patients. Three-dimensional visualization models were created for different types of anorectal abscesses, including perianal, intersphincteric, ischiorectal, and supralevator abscesses. No statistically significant differences were observed in the volume of the internal sphincter, external sphincter, and levator ani muscle between pre- and post-operative patients. The 3D model of anorectal abscess, reconstructed from MRI data, offers a precise and direct visualization of the anatomical structures associated with various types of anorectal abscesses. The infection did not result in any damage to the internal and external anal sphincter and levator ani muscle., (© 2024. The Author(s).)

To explore the advantages and disadvantages of 3D Slicer reconstruction and 3D printing localization combined with transcranial neuroendoscope in the surgical treatment of deep cerebral micro cavernous hemangiomas. Method The clinical data of patients with deep cerebral micro cavernous hemangiomas treated by our hospital from June 2022 to February 2023 using 3D Slicer reconstruction and 3D printing localization technology combined with transcranial endoscopic surgery were retrospectively analyzed. A total of 5 cases with complete data were collected, including 2 males and 3 females, aged 9-59 years. All 5 patients had deep supratentorial cavernous hemangiomas with a diameter of less than 1.5 cm, and had clinical symptoms such as headache or epilepsy, and had been diagnosed by CT or MRI. Repeated bleeding from small cavernous hemangiomas in the deep brain can lead to clinical symptoms such as recurrent headache and epilepsy, and is required surgical treatment. However, cavernous hemangiomas often have smaller lesions and are difficult to locate in the deep part. Without neuronavigation, surgery can become extremely difficult. Our team's newly developed 3D Slicer reconstruction and 3D printing localization technology which could provide new options for surgical treatment of small cavernous hemangiomas or other small lesions in the deep brain, but its accuracy and safety still need to be verified by further clinical research., (© 2024. The Author(s).)

High dose-rate brachytherapy is a treatment technique for gynecologic cancers where intracavitary applicators are placed within the patient's pelvic cavity. To ensure accurate radiation delivery, localization of the applicator at the time of insertion is vital. This study proposes a novel method for acquiring, registering, and fusing three-dimensional (3D) trans-abdominal and 3D trans-rectal ultrasound (US) images for visualization of the pelvic anatomy and applicators during gynecologic brachytherapy. The workflow was validated using custom multi-modal pelvic phantoms and demonstrated during two patient procedures. Experiments were performed for three types of intracavitary applicators: ring-and-tandem, ring-and-tandem with interstitial needles, and tandem-and-ovoids. Fused 3D US images were registered to magnetic resonance (MR) and computed tomography (CT) images for validation. The target registration error (TRE) and fiducial localization error (FLE) were calculated to quantify the accuracy of our fusion technique. For both phantom and patient images, TRE and FLE across all modality registrations (3D US versus MR or CT) resulted in mean ± standard deviation of 4.01 ± 1.01 mm and 0.43 ± 0.24 mm, respectively. This work indicates proof of concept for conducting further clinical studies leveraging 3D US imaging as an accurate, accessible alternative to advanced modalities for localizing brachytherapy applicators., (© 2024. The Author(s).)

We examined the association between texture features using three-dimensional (3D) io-dine density histogram on delayed phase of dual-energy CT (DECT) and expression of programmed death-ligand 1 (PD-L1) using immunostaining methods in non-small cell lung cancer. Consecutive 37 patients were scanned by DECT. Unenhanced and enhanced (3 min delay) images were obtained. 3D texture analysis was performed for each nodule to obtain 7 features (max, min, median, mean, standard deviation, skewness, and kurtosis) from iodine density mapping and extracellular volume (ECV). A pathologist evaluated a tumor proportion score (TPS, %) using PD-L1 immunostaining: PD-L1 high (TPS ≥ 50%) and low or negative expression (TPS < 50%). Associations between PD-L1 expression and each 8 parameter were evaluated using logistic regression analysis. The multivariate logistic regression analysis revealed that skewness and ECV were independent indicators associated with high PD-L1 expression (skewness: odds ratio [OR]  7.1 [95% CI 1.1, 45.6], p = 0.039; ECV: OR 6.6 [95% CI 1.1, 38.4], p = 0.037). In the receiver-operating characteristic analysis, the area under the curve of the combination of skewness and ECV was 0.83 (95% CI 0.67, 0.93) with sensitivity of 64% and specificity of 96%. Skewness from 3D iodine density histogram and ECV on dual energy CT were significant factors for predicting PD-L1 expression., (© 2024. The Author(s).)

A sensitive and efficient imaging technique is required to assess the subtle abnormalities occurring in the normal-appearing white matter (NAWM) and normal-appearing grey matter (NAGM) in patients with relapsing-remitting multiple sclerosis (RRMS). In this study, a fast 3D macromolecular proton fraction (MPF) quantification based on spin-lock (fast MPF-SL) sequence was proposed for brain MPF mapping. Thirty-four participants, including 17 healthy controls and 17 RRMS patients were prospectively recruited. We conducted group comparison and correlation between conventional MPF-SL, fast MPF-SL, and DWI, and compared differences in quantified parameters within MS lesions and the regional NAWM, NAGM, and normal-appearing deep grey matter (NADGN). MPF of MS lesions was significantly reduced (7.17% ± 1.15%, P < 0.01) compared to all corresponding normal-appearing regions. MS patients also showed significantly reduced mean MPF values compared with controls in NAGM (4.87% ± 0.38% vs 5.21% ± 0.32%, P = 0.01), NAWM (9.49% ± 0.69% vs 10.32% ± 0.59%, P < 0.01) and NADGM (thalamus 5.59% ± 0.67% vs 6.00% ± 0.41%, P = 0.04; caudate 5.10% ± 0.55% vs 5.53% ± 0.58%, P = 0.03). MPF and ADC showed abnormalities in otherwise normal appearing close to lesion areas (P < 0.01). In conclusion, time-efficient MPF mapping of the whole brain can be acquired efficiently (< 3 min) using fast MPF-SL. It offers a promising alternative way to detect white matter abnormalities in MS., (© 2024. The Author(s).)

The development of non-destructive, tomographic imaging systems is a current topic of research in biomedical technologies. One of these technologies is Scanning Laser Optical Tomography (SLOT), which features a highly modular setup with various contrast mechanisms. Extending this technology with new acquisition mechanisms allows us to investigate untreated and non-stained biological samples, leaving their natural biological physiology intact. To enhance the development of SLOT, we aimed to extend the density of information with a significant increase of acquisition channels. This should allow us to investigate samples with unknown emission spectra and even allow for label-fee cell identification. We developed and integrated a hyperspectral module into an existing SLOT system. The adaptations allow for the acquisition of three-dimensional datasets containing a highly increased information density. For validation, artificial test objects were made from fluorescent acrylic and acquired with the new hyperspectral setup. In addition, measurements were made on two different human cell spheroids with an unknown spectra, to test the possibilities of label-free cell identification. The validation measurements of the artificial test target show the expected results. Furthermore, the measurements of the biological cell spheroids show small variations in their tomographic spectrum that allow for label-free cell type differentiation. The results of the biological sample demonstrate the potential of label-free cell identification of the newly developed setup., (© 2024. The Author(s).)

Skin microvasculature is vital for human cardiovascular health and thermoregulation, but its imaging and analysis presents significant challenges. Statistical methods such as speckle decorrelation in optical coherence tomography angiography (OCTA) often require multiple co-located B-scans, leading to lengthy acquisitions prone to motion artefacts. Deep learning has shown promise in enhancing accuracy and reducing measurement time by leveraging local information. However, both statistical and deep learning methods typically focus solely on processing individual 2D B-scans, neglecting contextual information from neighbouring B-scans. This limitation compromises spatial context and disregards the 3D features within tissue, potentially affecting OCTA image accuracy. In this study, we propose a novel approach utilising 3D convolutional neural networks (CNNs) to address this limitation. By considering the 3D spatial context, these 3D CNNs mitigate information loss, preserving fine details and boundaries in OCTA images. Our method reduces the required number of B-scans while enhancing accuracy, thereby increasing clinical applicability. This advancement holds promise for improving clinical practices and understanding skin microvascular dynamics crucial for cardiovascular health and thermoregulation., (© 2024. The Author(s).)

American white (Quercus alba L.) oak casks have been used for liquid storage for centuries. Their use in aged spirits is critical to imparting flavor and mouthfeel to the final product. The reason that barrels retain liquid has been hypothesized to be the result of abundant physiological structures called tyloses in parenchyma tissues and medullary rays in white oak. Using non-destructive X-ray computed tomography (XRCT) imaging, we reveal an unprecedented view of tylose structure and quantify the pore-filling capacity of tyloses in white oak that underscores the liquid retention we observe in casks. We show that pores of white oaks are filled with sevenfold higher tylose volume compared to northern red oak (Q. rubra), consistent with prior literature that casks made from white oak retain liquid while red oak fails to do so. We propose that XRCT represents a methodological standard for observing these complex structures and should be employed to understand the many questions related to liquid losses from casks, cultural treatment of casks, and the influence of climate change on oak tyloses in the future., (© 2024. The Author(s).)

The size and axial alignment of prostheses, when planned during total knee replacement (TKA) are critical for recovery of knee function and improvement of knee pain symptoms. This research aims to study the effect of artificial intelligence (AI)-based preoperative three dimensional (3D) planning technology on prosthesis size and axial alignment planning in TKA, and to compare its advantages with two dimensional (2D) X-ray template measurement technology. A total of 60 patients with knee osteoarthritis (KOA) who underwent TKA for the first time were included in the AI (n = 30) and 2D (n = 30) groups. The preoperative and postoperative prosthesis size, femoral valgus correction angle (VCA) and hip-knee-ankle angle (HKA) were recorded and compared between the two groups. The results of the University of Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and the American Knee Association Score (AKS) were evaluated before surgery, 3 months, 6 months, and 12 months after surgery. The accuracy of prosthesis size, VCA and HKA prediction in AI group was significantly higher than that in 2D group (P < 0.05). The WOMAC and AKS scores in AI group at 3 months, 6 months and 12 months after surgery were better than those in 2D group (P < 0.05). Both groups showed significant improvement in WOMAC and AKS scores at 12 months follow-up. AI-based preoperative 3D planning technique has more reliable planning effect for prosthesis size and axial alignment in TKA., (© 2024. The Author(s).)

To quantitatively evaluate chronic kidney disease (CKD), a deep convolutional neural network-based segmentation model was applied to renal enhanced computed tomography (CT) images. A retrospective analysis was conducted on a cohort of 100 individuals diagnosed with CKD and 90 individuals with healthy kidneys, who underwent contrast-enhanced CT scans of the kidneys or abdomen. Demographic and clinical data were collected from all participants. The study consisted of two distinct stages: firstly, the development and validation of a three-dimensional (3D) nnU-Net model for segmenting the arterial phase of renal enhanced CT scans; secondly, the utilization of the 3D nnU-Net model for quantitative evaluation of CKD. The 3D nnU-Net model achieved a mean Dice Similarity Coefficient (DSC) of 93.53% for renal parenchyma and 81.48% for renal cortex. Statistically significant differences were observed among different stages of renal function for renal parenchyma volume (V RP ), renal cortex volume (V RC ), renal medulla volume (V RM ), the CT values of renal parenchyma (Hu RP ), the CT values of renal cortex (Hu RC ), and the CT values of renal medulla (Hu RM ) (F = 93.476, 144.918, 9.637, 170.533, 216.616, and 94.283; p < 0.001). Pearson correlation analysis revealed significant positive associations between glomerular filtration rate (eGFR) and V RP , V RC , V RM , Hu RP , Hu RC , and Hu RM (r = 0.749, 0.818, 0.321, 0.819, 0.820, and 0.747, respectively, all p < 0.001). Similarly, a negative correlation was observed between serum creatinine (Scr) levels and V RP , V RC , V RM , Hu RP , Hu RC , and Hu RM (r = - 0.759, - 0.777, - 0.420, - 0.762, - 0.771, and - 0.726, respectively, all p < 0.001). For predicting CKD in males, V RP had an area under the curve (AUC) of 0.726, p < 0.001; V RC , AUC 0.765, p < 0.001; V RM , AUC 0.578, p = 0.018; Hu RP , AUC 0.912, p < 0.001; Hu RC , AUC 0.952, p < 0.001; and Hu RM , AUC 0.772, p < 0.001 in males. In females, V RP had an AUC of 0.813, p < 0.001; V RC , AUC 0.851, p < 0.001; V RM , AUC 0.623, p = 0.060; Hu RP , AUC 0.904, p < 0.001; Hu RC , AUC 0.934, p < 0.001; and Hu RM , AUC 0.840, p < 0.001. The optimal cutoff values for predicting CKD in Hu RP are 99.9 Hu for males and 98.4 Hu for females, while in Hu RC are 120.1 Hu for males and 111.8 Hu for females. The kidney was effectively segmented by our AI-based 3D nnU-Net model for enhanced renal CT images. In terms of mild kidney injury, the CT values exhibited higher sensitivity compared to kidney volume. The correlation analysis revealed a stronger association between V RC , Hu RP , and Hu RC with renal function, while the association between V RP and Hu RM was weaker, and the association between V RM was the weakest. Particularly, Hu RP and Hu RC demonstrated significant potential in predicting renal function. For diagnosing CKD, it is recommended to set the threshold values as follows: Hu RP  < 99.9 Hu and Hu RC  < 120.1 Hu in males, and Hu RP  < 98.4 Hu and Hu RC  < 111.8 Hu in females., (© 2024. The Author(s).)

When conducting spine-related diagnosis and surgery, the three-dimensional (3D) upright posture of the spine under natural weight bearing is of significant clinical value for physicians to analyze the force on the spine. However, existing medical imaging technologies cannot meet current requirements of medical service. On the one hand, the mainstream 3D volumetric imaging modalities (e.g. CT and MRI) require patients to lie down during the imaging process. On the other hand, the imaging modalities conducted in an upright posture (e.g. radiograph) can only realize 2D projections, which lose the valid information of spinal anatomy and curvature. Developments of deep learning-based 3D reconstruction methods bring potential to overcome the limitations of the existing medical imaging technologies. To deal with the limitations of current medical imaging technologies as is described above, in this paper, we propose a novel deep learning framework, ReVerteR, which can realize automatic 3D Reconstruction of Vertebrae from orthogonal bi-planar Radiographs. With the utilization of self-attention mechanism and specially designed loss function combining Dice, Hausdorff, Focal, and MSE, ReVerteR can alleviate the sample-imbalance problem during the reconstruction process and realize the fusion of the centroid annotation and the focused vertebra. Furthermore, aiming at automatic and customized 3D spinal reconstruction in real-world scenarios, we extend ReVerteR to a clinical deployment-oriented framework, and develop an interactive interface with all functions in the framework integrated so as to enhance human-computer interaction during clinical decision-making. Extensive experiments and visualization conducted on our constructed datasets based on two benchmark datasets of spinal CT, VerSe 2019 and VerSe 2020, demonstrate the effectiveness of our proposed ReVerteR. In this paper, we propose an automatic 3D reconstruction method of vertebrae based on orthogonal bi-planar radiographs. With the 3D upright posture of the spine under natural weight bearing effectively constructed, our proposed method is expected to better support doctors make clinical decision during spine-related diagnosis and surgery., (© 2024. The Author(s).)

Traditionally, constructing training datasets for automatic muscle segmentation from medical images involved skilled operators, leading to high labor costs and limited scalability. To address this issue, we developed a tool that enables efficient annotation by non-experts and assessed its effectiveness for training an automatic segmentation network. Our system allows users to deform a template three-dimensional (3D) anatomical model to fit a target magnetic-resonance image using free-form deformation with independent control points for axial, sagittal, and coronal directions. This method simplifies the annotation process by allowing non-experts to intuitively adjust the model, enabling simultaneous annotation of all muscles in the template. We evaluated the quality of the tool-assisted segmentation performed by non-experts, which achieved a Dice coefficient greater than 0.75 compared to expert segmentation, without significant errors such as mislabeling adjacent muscles or omitting musculature. An automatic segmentation network trained with datasets created using this tool demonstrated performance comparable to or superior to that of networks trained with expert-generated datasets. This innovative tool significantly reduces the time and labor costs associated with dataset creation for automatic muscle segmentation, potentially revolutionizing medical image annotation and accelerating the development of deep learning-based segmentation networks in various clinical applications., (© 2024. The Author(s).)

A three-dimensional convolutional neural network model was developed to classify the severity of chronic kidney disease (CKD) using magnetic resonance imaging (MRI) Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) imaging. Seventy-three patients with severe renal dysfunction (estimated glomerular filtration rate [eGFR] < 30 mL/min/1.73 m 2 , CKD stage G4-5); 172 with moderate renal dysfunction (30 ≤ eGFR < 60 mL/min/1.73 m 2 , CKD stage G3a/b); and 76 with mild renal dysfunction (eGFR ≥ 60 mL/min/1.73 m 2 , CKD stage G1-2) participated in this study. The model was applied to the right, left, and both kidneys, as well as to each imaging method (T1-weighted IP/OP/WO images). The best performance was obtained when using bilateral kidneys and IP images, with an accuracy of 0.862 ± 0.036. The overall accuracy was better for the bilateral kidney models than for the unilateral kidney models. Our deep learning approach using kidney MRI can be applied to classify patients with CKD based on the severity of kidney disease., (© 2024. The Author(s).)