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Learning cardiac morphology largely involves spatial abilities and studies indicate benefits from innovative 3D visualization technologies that speed up and increase the learning output. Studies comparing these teaching tools and their educational output are rare and few studies include complex congenital heart defects. This study compared the effects of 3D prints, mixed reality (MR) viewing of 3D meshes and standard cardiac CT slice images on medical students' understanding of complex congenital heart defect morphology, measuring both objective level of understanding and subjective educational experience. The objective of this study was to compare morphological understanding and user experiences of 3D printed models, MR 3D visualization and axial 2D CT slices, in medical students examining morphological details in complex congenital heart defects. Medical students in the median 4th year of study (range 2nd to 6th) examined three of five different complex congenital heart defects by three different modalities: 3D printed model, MR viewed 3D mesh, and cardiac CT slices, answering a questionnaire on morphology and user experience. Time to complete task, diagnostic accuracy, and user experience data were collected and compared on group level. Task times were similar for all modalities. The percentage of correct answers was higher with MR visualization, which was also the preferred modality overall. Medical students both prefer and better understand the morphology of complex congenital heart disease with 3D models viewed using MR, without spending more time than with 3D prints or 2D CT images., (© 2024 The Author(s). Anatomical Sciences Education published by Wiley Periodicals LLC on behalf of American Association for Anatomy.)

To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. The ATM'22 dataset includes a large group of COVID'19 patients and a range of other lung diseases, however, relatively few patients with severe pathologies affecting the airway tree anatomy was found. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Støverud et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background and Objective: Patients suspected to have lung cancer, undergo endobronchial ultrasound bronchoscopy (EBUS) for the purpose of diagnosis and staging. For presumptive curable patients, the EBUS bronchoscopy is planned based on images and data from computed tomography (CT) images and positron emission tomography (PET). Our study aimed to evaluate the feasibility of a multimodal electromagnetic navigation platform for EBUS bronchoscopy, integrating ultrasound and segmented CT, and PET scan imaging data., Methods: The proof-of-concept study included patients with suspected lung cancer and pathological mediastinal/hilar lymph nodes identified on both CT and PET scans. Images obtained from these two modalities were segmented to delineate target lymph nodes and then incorporated into the CustusX navigation platform. The EBUS bronchoscope was equipped with a sensor, calibrated, and affixed to a 3D printed click-on device positioned at the bronchoscope's tip. Navigation accuracy was measured postoperatively using ultrasound recordings., Results: The study enrolled three patients, all presenting with suspected mediastinal lymph node metastasis (N1-3). All PET-positive lymph nodes were displayed in the navigation platform during the EBUS procedures. In total, five distinct lymph nodes were sampled, yielding malignant cells from three nodes and lymphocytes from the remaining two. The median accuracy of the navigation system was 7.7 mm., Conclusion: Our study introduces a feasible multimodal electromagnetic navigation platform that combines intraoperative ultrasound with preoperative segmented CT and PET imaging data for EBUS lymph node staging examinations. This innovative approach holds promise for enhancing the accuracy and effectiveness of EBUS procedures., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kildahl-Andersen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Endobronchial ultrasound (EBUS) is used in the minimally invasive sampling of thoracic lymph nodes. In lung cancer staging, the accurate assessment of mediastinal structures is essential but challenged by variations in anatomy, image quality, and operator-dependent image interpretation. This study aimed to automatically detect and segment mediastinal lymph nodes and blood vessels employing a novel U-Net architecture-based approach in EBUS images. A total of 1161 EBUS images from 40 patients were annotated. For training and validation, 882 images from 30 patients and 145 images from 5 patients were utilized. A separate set of 134 images was reserved for testing. For lymph node and blood vessel segmentation, the mean ± standard deviation (SD) values of the Dice similarity coefficient were 0.71 ± 0.35 and 0.76 ± 0.38, those of the precision were 0.69 ± 0.36 and 0.82 ± 0.22, those of the sensitivity were 0.71 ± 0.38 and 0.80 ± 0.25, those of the specificity were 0.98 ± 0.02 and 0.99 ± 0.01, and those of the F1 score were 0.85 ± 0.16 and 0.81 ± 0.21, respectively. The average processing and segmentation run-time per image was 55 ± 1 ms (mean ± SD). The new U-Net architecture-based approach (EBUS-AI) could automatically detect and segment mediastinal lymph nodes and blood vessels in EBUS images. The method performed well and was feasible and fast, enabling real-time automatic labeling., Competing Interests: Øyvind Ervik reports one lecture fee from MSD. Hanne Sorger reports one lecture fee from AstraZeneca. For the remaining authors, there are no conflicts of interest or other disclosures.

Machine learning and deep learning are novel methods which are revolutionizing medical imaging. In our study we trained an algorithm with a U-Net shaped network to recognize ultrasound images of the median nerve in the complete distal half of the forearm and to measure the cross-sectional area at the inlet of the carpal tunnel. Images of 25 patient hands with carpal tunnel syndrome (CTS) and 26 healthy controls were recorded on a video loop covering 15 cm of the distal forearm and 2355 images were manually segmented. We found an average Dice score of 0.76 between manual and automated segmentation of the median nerve in its complete course, while the measurement of the cross-sectional area at the carpal tunnel inlet resulted in a 10.9% difference between manually and automated measurements. We regard this technology as a suitable device for verifying the diagnosis of CTS., (© 2024. The Author(s).)

Background: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images., Methods: A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dental clinicians, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models., Results: The trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean false negative rate. Whereas the best annotators on each of these metrics reported 0.299, 0.495, and 0.164 respectively., Conclusion: Deep-learning models have shown the potential to assist dental professionals in the diagnosis of caries. Yet, the task remains challenging due to the artifacts natural to the bitewing images., (© 2024. The Author(s).)

The purpose of this study was to evaluate the accuracy of a method for estimating the tip position of a fiber optic shape-sensing (FOSS) integrated instrument being inserted through a bronchoscope. A modified guidewire with a multicore optical fiber was inserted into the working channel of a custom-made catheter with three electromagnetic (EM) sensors. The displacement between the instruments was manually set, and a point-based method was applied to match the position of the EM sensors to corresponding points on the shape. The accuracy was evaluated in a realistic bronchial model. An additional EM sensor was used to sample the tip of the guidewire, and the absolute deviation between this position and the estimated tip position was calculated. For small displacements between the tip of the FOSS integrated tool and the catheter, the median deviation in estimated tip position was ≤5 mm. For larger displacements, deviations exceeding 10 mm were observed. The deviations increased when the shape sensor had sharp curvatures relative to more straight shapes. The method works well for clinically relevant displacements of a biopsy tool from the bronchoscope tip, and when the path to the lesion has limited curvatures. However, improvements must be made to our configuration before pursuing further clinical testing., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction and aims: During an Endovascular Aneurysm Repair (EVAR) procedure a stiff guidewire is inserted from the iliac arteries. This induces significant deformations on the vasculature, thus, affecting the pre-operative planning, and the accuracy of image fusion. The aim of the present work is to predict the guidewire induced deformations using a finite element approach validated through experiments with patient-specific additive manufactured models. The numerical approach herein developed could improve the pre-operative planning and the intra-operative navigation. Material and methods: The physical models used for the experiments in the hybrid operating room, were manufactured from the segmentations of pre-operative Computed Tomography (CT) angiographies. The finite element analyses (FEA) were performed with LS-DYNA Explicit. The material properties used in finite element analyses were obtained by uniaxial tensile tests. The experimental deformed configurations of the aorta were compared to those obtained from FEA. Three models, obtained from Computed Tomography acquisitions, were investigated in the present work: A) without intraluminal thrombus (ILT), B) with ILT, C) with ILT and calcifications. Results and discussion: A good agreement was found between the experimental and the computational studies. The average error between the final in vitro vs. in silico aortic configurations, i.e., when the guidewire is fully inserted, are equal to 1.17, 1.22 and 1.40 mm, respectively, for Models A, B and C. The increasing trend in values of deformations from Model A to Model C was noticed both experimentally and numerically. The presented validated computational approach in combination with a tracking technology of the endovascular devices may be used to obtain the intra-operative configuration of the vessels and devices prior to the procedure, thus limiting the radiation exposure and the contrast agent dose., Competing Interests: Author PD was employed by HSL S.r.l. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Emendi, Støverud, Tangen, Ulsaker, Manstad-H, Di Giovanni, Dahl, Langø and Prot.)

Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging., Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting., Results: Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime., Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 de Frutos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: Despite extensive preoperative imaging, intraoperative localization of liver lesions after systemic treatment can be challenging. Therefore, an image-guided navigation setup is explored that links preoperative diagnostic scans and 3D models to intraoperative ultrasound (US), enabling overlay of detailed diagnostic images on intraoperative US. Aim of this study is to assess the workflow and accuracy of such a navigation system which compensates for liver motion., Methods: Electromagnetic (EM) tracking was used for organ tracking and movement of the transducer. After laparotomy, a sensor was attached to the liver surface while the EM-tracked US transducer enabled image acquisition and landmark digitization. Landmarks surrounding the lesion were selected during patient-specific preoperative 3D planning and identified for registration during surgery. Endpoints were accuracy and additional times of the investigative steps. Accuracy was computed at the center of the target lesion., Results: In total, 22 navigated procedures were performed. Navigation provided useful visualization of preoperative 3D models and their overlay on US imaging. Landmark-based registration resulted in a mean fiducial registration error of 10.3 ± 4.3 mm, and a mean target registration error of 8.5 ± 4.2 mm. Navigation was available after an average of 12.7 min., Conclusion: We developed a navigation method combining ultrasound with active liver tracking for organ motion compensation, with an accuracy below 10 mm. Fixation of the liver sensor near the target lesion compensates for local movement and contributes to improved reliability during navigation. This represents an important step forward in providing surgical navigation throughout the procedure., Trial Registration: This study is registered in the Netherlands Trial Register (number NL7951)., (© 2022. CARS.)

Introduction: The established method for assessment of mediastinal and hilar lymph nodes is endobronchial ultrasound bronchoscopy (EBUS) with needle aspirations. Previously, we presented an electromagnetic navigation platform for this purpose. There were several issues with the permanent electromagnetic tracking (EMT) sensor attachment on the tip of the experimental EBUS bronchoscope. The purpose was to develop a device for on-site attachment of the EMT sensor., Material and Methods: A clip-on EMT sensor attachment device was 3D-printed in Ultem™ and attached to an EBUS bronchoscope. A specially designed ultrasound probe calibration adapter was developed for on-site and quick probe calibration. Navigation accuracy was studied using a wire cross water phantom and clinical feasibility was tested in a healthy volunteer., Results: The device attached to the EBUS bronchoscope increased its diameter from 6.9 mm to 9.5 mm. Average preclinical navigation accuracy was 3.9 mm after adapter calibration. The maneuvering of the bronchoscope examining a healthy volunteer was adequate without harming the respiratory epithelium, and the device stayed firmly attached., Conclusion: Development, calibration and testing of a clip-on EMT sensor attachment device for EBUS bronchoscopy was successfully demonstrated. Acceptable accuracy results were obtained, and the device is ready to be tested in patient studies.

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: Cancer is among the leading causes of death in the developed world, and lung cancer is the most lethal type. Early detection is crucial for better prognosis, but can be resource intensive to achieve. Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutional neural networks may be suited for such tasks, but require substantial amounts of labeled data to train. Obtaining labeled data is a challenge, especially in the medical domain., Methods: This paper investigates the use of a teacher-student design to utilize datasets with different types of supervision to train an automatic model performing pulmonary tumor segmentation on computed tomography images. The framework consists of two models: the student that performs end-to-end automatic tumor segmentation and the teacher that supplies the student additional pseudo-annotated data during training., Results: Using only a small proportion of semantically labeled data and a large number of bounding box annotated data, we achieved competitive performance using a teacher-student design. Models trained on larger amounts of semantic annotations did not perform better than those trained on teacher-annotated data. Our model trained on a small number of semantically labeled data achieved a mean dice similarity coefficient of 71.0 on the MSD Lung dataset., Conclusions: Our results demonstrate the potential of utilizing teacher-student designs to reduce the annotation load, as less supervised annotation schemes may be performed, without any real degradation in segmentation accuracy., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Effectiveness of e-learning diminishes without the support of a pedagogical model to guide its use. In minimally invasive surgery (MIS), this has been reported as a limitation when technology is used to deliver contents without a sound pedagogical background., Material and Methods: We describe how a generic pedagogical model, the 3D pedagogy framework, can be used for setting learning outcomes and activities in e-learning platforms focused on MIS cognitive skills. A demonstrator course on Nissen fundoplication was developed following the model step-by-step in the MISTELA learning platform. Course design was informed by Kolb's Experiential learning model. Content validation was performed by 13 MIS experts., Results: Ten experts agreed on the suitability of content structuring done according to the pedagogical model. All experts agreed that the course provides means to assess the intended learning outcomes., Conclusions: This work showcases how a general-purpose e-learning framework can be accommodated to the needs of MIS training without limiting the course designers' pedagogical approach. Key advances for its success include: (1) proving the validity of the model in the wider scope of MIS skills and (2) raising awareness amongst stakeholders on the need of developing training plans with explicit, rather than assumed, pedagogical foundations. Abbreviations: MIS: minimally invasive surgery; TEL: technology enhanced learning.

Purpose: To present a novel methodical approach to compare visibility of percutaneous needles in ultrasound images., Methods: A motor-driven rotation platform was used to gradually change the needle angle while capturing image data. Data analysis was automated using block-matching-based registration, with a tracking and refinement step. Every 25 frames, a Hough transform was used to improve needle alignments after large rotations. The method was demonstrated by comparing three commercial needles (14G radiofrequency ablation, RFA; 18G Trocar; 22G Chiba) and six prototype needles with different sizes, materials, and surface conditions (polished, sand-blasted, and kerfed), within polyvinyl alcohol phantom tissue and ex vivo bovine liver models. For each needle and angle, a contrast-to-noise ratio (CNR) was determined to quantify visibility. CNR values are presented as a function of needle type and insertion angle. In addition, the normalized area under the (CNR-angle) curve was used as a summary metric to compare needles., Results: In phantom tissue, the first kerfed needle design had the largest normalized area of visibility and the polished 1 mm diameter stainless steel needle the smallest (0.704 ± 0.199 vs. 0.154 ± 0.027, p < 0.01). In the ex vivo model, the second kerfed needle design had the largest normalized area of visibility, and the sand-blasted stainless steel needle the smallest (0.470 ± 0.190 vs. 0.127 ± 0.047, p < 0.001). As expected, the analysis showed needle visibility peaks at orthogonal insertion angles. For acute or obtuse angles, needle visibility was similar or reduced. Overall, the variability in needle visibility was considerably higher in livers., Conclusion: The best overall visibility was found with kerfed needles and the commercial RFA needle. The presented methodical approach to quantify ultrasound visibility allows comparisons of (echogenic) needles, as well as other technological innovations aiming to improve ultrasound visibility of percutaneous needles, such as coatings, material treatments, and beam steering approaches., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)