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This proactive approach can lead to improved patient outcomes, reduced complications, and enhanced recovery rates, thereby bolstering the physician's reputation and patient satisfaction. Using insights from the study, physicians can ensure that patients have the best potential outcomes from dental implant procedures, especially in terms of minimizing bone loss. Lower radiation exposure and potentially shorter imaging time can enhance the patient's comfort and experience, which indirectly benefits the physician by facilitating easier patient management and improved patient compliance. Modern medical imaging systems provide ever-more information about the patient's health condition. [Extracted from the article]

Background: High-content screening (HCS) is a pre-clinical approach for the assessment of drug efficacy. On modern platforms, it involves fluorescent image capture using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D images is an essential prerequisite to quantify captured fluorescence in cells for screening. However, this segmentation is challenging due to variabilities in cell confluency, drug-induced alterations in cell morphology, and gradual degradation of fluorescence with the depth of scanning. Despite advances in algorithms for segmenting nuclei for HCS, robust 3D methods that are insensitive to these conditions are still lacking. Results: We have developed an algorithm which first generates a 3D nuclear mask in the original images. Next, an iterative 3D marker-controlled watershed segmentation is applied to downsized images to segment adjacent nuclei under the mask. In the last step, borders of segmented nuclei are adjusted in the original images based on local nucleus and background intensities. The method was developed using a set of 10 3D images. Extensive tests on a separate set of 27 3D images containing 2,367 nuclei demonstrated that our method, in comparison with 6 reference methods, achieved the highest precision (PR = 0.97), recall (RE = 0.88) and F1-score (F1 = 0.93) of nuclei detection. The Jaccard index (JI = 0.83), which reflects the accuracy of nuclei delineation, was similar to that yielded by all reference approaches. Our method was on average more than twice as fast as the reference method that produced the best results. Additional tests carried out on three stacked 3D images comprising heterogenous nuclei yielded average PR = 0.96, RE = 0.84, F1 = 0.89, and JI = 0.80. Conclusions: The high-performance metrics yielded by the proposed approach suggest that it can be used to reliably delineate nuclei in 3D images of monolayered and stacked cells exposed to cytotoxic drugs. [ABSTRACT FROM AUTHOR]

Background: High-content screening (HCS) is a pre-clinical approach for the assessment of drug efficacy. On modern platforms, it involves fluorescent image capture using three-dimensional (3D) scanning microscopy. Segmentation of cell nuclei in 3D images is an essential prerequisite to quantify captured fluorescence in cells for screening. However, this segmentation is challenging due to variabilities in cell confluency, drug-induced alterations in cell morphology, and gradual degradation of fluorescence with the depth of scanning. Despite advances in algorithms for segmenting nuclei for HCS, robust 3D methods that are insensitive to these conditions are still lacking. Results: We have developed an algorithm which first generates a 3D nuclear mask in the original images. Next, an iterative 3D marker-controlled watershed segmentation is applied to downsized images to segment adjacent nuclei under the mask. In the last step, borders of segmented nuclei are adjusted in the original images based on local nucleus and background intensities. The method was developed using a set of 10 3D images. Extensive tests on a separate set of 27 3D images containing 2,367 nuclei demonstrated that our method, in comparison with 6 reference methods, achieved the highest precision (PR = 0.97), recall (RE = 0.88) and F1-score (F1 = 0.93) of nuclei detection. The Jaccard index (JI = 0.83), which reflects the accuracy of nuclei delineation, was similar to that yielded by all reference approaches. Our method was on average more than twice as fast as the reference method that produced the best results. Additional tests carried out on three stacked 3D images comprising heterogenous nuclei yielded average PR = 0.96, RE = 0.84, F1 = 0.89, and JI = 0.80. Conclusions: The high-performance metrics yielded by the proposed approach suggest that it can be used to reliably delineate nuclei in 3D images of monolayered and stacked cells exposed to cytotoxic drugs. [ABSTRACT FROM AUTHOR]

The selection of the matrix size is an important element of the magnetic resonance imaging (MRI) process, and has a significant impact on the acquired image quality. Signal to noise ratio, often used to assess MR image quality, has its limitations. Thus, for this purpose we propose a novel approach: the use of texture analysis as an index of the image quality that is sensitive for the change of matrix size. Image texture in biomedical images represents tissue and organ structures visualized via medical imaging modalities such as MRI. The correlation between texture parameters determined for the same tissues visualized in images acquired with different matrix sizes is analyzed to aid in the assessment of the selection of the optimal matrix size. T2-weighted coronal images of shoulders were acquired using five different matrix sizes while maintaining the same field of view; three regions of interest (bone, fat, and muscle) were considered. Lin's correlation coefficients were calculated for all possible pairs of the 310-element texture feature vectors evaluated for each matrix. The obtained results are discussed considering the image noise and blurring effect visible in images acquired with smaller matrices. Taking these phenomena into account, recommendations for the selection of the matrix size used for the MRI imaging were proposed. [ABSTRACT FROM AUTHOR]

Correlation of parametrized image texture features (ITF) analyses conducted in different regions of interest (ROIs) overcomes limitations and reliably reflects image quality. The aim of this study is to propose a nonparametrical method and classify the quality of a magnetic resonance (MR) image that has undergone controlled degradation by using textural features in the image. Images of 41 patients, 17 women and 24 men, aged between 23 and 56 years were analyzed. T2-weighted sagittal sequences of the lumbar spine, cervical spine, and knee and T2-weighted coronal sequences of the shoulder and wrist were generated. The implementation of parallel imaging with the use of GRAPPA2, GRAPPA3, and GRAPPA4 led to a substantial reduction in the scanning time but also degraded image quality. The number of degraded image textural features was correlated with the scanning time. Longer scan times correlated with markedly higher ITF image persistence in comparison with images computed with reduced scan times. Higher ITF preservation was observed in images of bones in the spine and femur as compared to images of soft tissues, i.e., tendons and muscles. Finally, a nonparametrized image quality assessment based on an analysis of the ITF, computed for different tissues, correlating with the changes in acquisition time of the MR images, was successfully developed. The correlation between acquisition time and the number of reproducible features present in an MR image was found to yield the necessary assumptions to calculate the quality index. [ABSTRACT FROM AUTHOR]

Ultrasound (US) imaging is one of the most popular techniques used in clinical diagnosis, mainly due to lack of adverse effects on patients and the simplicity of US equipment. However, the characteristics of the medium cause US imaging to imprecisely reconstruct examined tissues. The artifacts are the results of wave phenomena, i.e. diffraction or refraction, and should be recognized during examination to avoid misinterpretation of an US image. Currently, US training is based on teaching materials and simulators and ultrasound simulation has become an active research area in medical computer science. Many US simulators are limited by the complexity of the wave phenomena, leading to intensive sophisticated computation that makes it difficult for systems to operate in real time. To achieve the required frame rate, the vast majority of simulators reduce the problem of wave diffraction and refraction. The following paper proposes a solution for an ultrasound simulator based on methods known in geophysics. To improve simulation quality, a wavefront construction method was adapted which takes into account the refraction phenomena. This technique uses ray tracing and velocity averaging to construct wavefronts in the simulation. Instead of a geological medium, real CT scans are applied. This approach can produce more realistic projections of pathological findings and is also capable of providing real-time simulation. [ABSTRACT FROM AUTHOR]

Simulators are a new tool in education in many fields, including medicine, where they greatly improve familiarity with medical procedures, reduce costs, and, importantly, cause no harm to patients. This is so in the case of transesophageal echocardiography (TEE), in which the use of a simulator facilitates spatial orientation and helps in case studies. The aim of the project described in this paper is to simulate an examination by TEE. This research makes use of available computed tomography data to simulate the corresponding echocardiographic view. This paper describes the essential characteristics that distinguish these two modalities and the key principles of the wave phenomena that should be considered in the simulation process, taking into account the conditions specific to the echocardiography. The construction of the CT2TEE (Web-based TEE simulator) is also presented. The considerations include ray-tracing and ray-casting techniques in the context of ultrasound beam and artifact simulation. An important aspect of the interaction with the user is raised. [ABSTRACT FROM PUBLISHER]

Objectives: The purpose of this paper is to assess the determination of male and female sex from trabecular bone structures in the pelvic region. The study involved analyzing digital radiographs for 343 patients and identifying fourteen areas of interest based on their medical significance, with seven regions on each side of the body for symmetry. Methods: Textural parameters for each region were obtained using various methods, and a thorough investigation of data normalization was conducted. Feature selection approaches were then evaluated to determine a small set of the most representative features, which were input into several classification machine learning models. Results: The findings revealed a sex-dependent correlation in the bone structure observed in X-ray images, with the degree of dependency varying based on the anatomical location. Notably, the femoral neck and ischium regions exhibited distinctive characteristics between sexes. Conclusions: This insight is crucial for medical professionals seeking to estimate sex dependencies from such image data. For these four specific areas, the balanced accuracy exceeded 70%. The results demonstrated symmetry, confirming the genuine dependencies in the trabecular bone structures. [ABSTRACT FROM AUTHOR]

Tissue factor (TF) and activated factor XI (FXIa) have been associated with acute coronary syndrome, ischemic stroke and venous thromboembolism. Their predictive value in stable coronary artery disease (CAD) is unclear. We investigated whether active TF and FXIa were associated with clinical outcomes in CAD patients in long-term observation. In 124 stable patients with multivessel CAD, we assessed the presence of circulating, active TF and FXIa by measuring a response of thrombin generation to respective inhibitory antibodies. We recorded the composite endpoint of myocardial infarction (MI), stroke, systemic thromboembolism and cardiovascular death during follow-up (median 106 months, interquartile range 95–119). Circulating FXIa and active TF were detected in 40% and 20.8% of the 120 patients (aged 65.0 [57.0–70.3] years, men, 78.3%), who completed follow-up. The composite endpoint occurred more frequently in patients with detectable active TF and FXIa present at baseline (hazard ratio [HR] 4.02, 95% confidence interval [CI] 2.26–7.17, p < 0.001 and HR 6.21, 95% CI 3.40–11.40, p < 0.001, respectively). On multivariate analysis FXIa, but not active TF, was an independent predictor of the composite endpoint, as well as MI, stroke/systemic thromboembolism, and cardiovascular death, when analyzed separately. To our knowledge, this study is the first to show that circulating FXIa predicts arterial thromboembolic events in advanced CAD, supporting a growing interest in FXIa inhibitors as novel antithrombotic agents. [Display omitted] • The prognostic role of active tissue factor (TF) and activated FXI (FXIa) in coronary artery disease (CAD) is unclear. • We investigated the impact of active TF and FXIa on long-term outcomes in CAD patients. • FXIa and, to a lesser extent, active TF were associated with cardiovascular events in CAD. • Targeting FXIa might represent a promising preventive strategy in high-risk CAD patients. [ABSTRACT FROM AUTHOR]

The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced, investigated, and experimentally compared with state-of-the-art NR-IQA methods on two representative MRIQA benchmark datasets. One of the datasets is introduced in this work. As the experimental validation reveals, the proposed fusion of networks outperforms related approaches in terms of correlation with subjective opinions of a large number of experienced radiologists. [ABSTRACT FROM AUTHOR]

In this paper, the problem of automation of dendrochronological measurements is considered. In particular, a fully automatic, image-based approach for detecting tree-ring boundaries in wood core images is introduced. The method is based on image gradient peak detection and linking. Unlike other existing approaches, the proposed method performs well on a variety of wood types and corresponding tree species. The results of applying the proposed approach to scanned images of wood cores representing 12 tree species (4 conifer and 8 angiosperm) are presented and discussed. Analysis of the results shows that the method performs almost faultlessly for conifer species, detecting almost 100% of tree-ring boundaries. In the case of diffuse-porous species, the results are not quite as good, but still at a level of 85% of properly detected tree-ring boundaries. For ring-porous species, the method has some problems, which are described in the paper. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate whether textural analysis could differentiate between the two common types of lytic lesions imaged with use of radiography. Sixty-two patients were enrolled in the study with intraoral radiograph images and a histological reference study. Full textural analysis was performed using MaZda software. For over 10,000 features, logistic regression models were applied. Fragments containing lesion edges were characterized by significant correlation of structural information. Although the input images were stored using lossy compression and their scale was not preserved, the obtained results confirmed the possibility of distinguishing between cysts and granulomas with use of textural analysis of intraoral radiographs. It was shown that the important information distinguishing the aforementioned types of lesions is located at the edges and not within the lesion. [ABSTRACT FROM AUTHOR]

An investigation of diseases using magnetic resonance (MR) imaging requires automatic image quality assessment methods able to exclude low-quality scans. Such methods can be also employed for an optimization of parameters of imaging systems or evaluation of image processing algorithms. Therefore, in this paper, a novel blind image quality assessment (BIQA) method for the evaluation of MR images is introduced. It is observed that the result of filtering using non-maximum suppression (NMS) strongly depends on the perceptual quality of an input image. Hence, in the method, the image is first processed by the NMS with various levels of acceptable local intensity difference. Then, the quality is efficiently expressed by the entropy of a sequence of extrema numbers obtained with the thresholded NMS. The proposed BIQA approach is compared with ten state-of-the-art techniques on a dataset containing MR images and subjective scores provided by 31 experienced radiologists. The Pearson, Spearman, Kendall correlation coefficients and root mean square error for the method assessing images in the dataset were 0.6741, 0.3540, 0.2428, and 0.5375, respectively. The extensive experimental evaluation of the BIQA methods reveals that the introduced measure outperforms related techniques by a large margin as it correlates better with human scores. [ABSTRACT FROM AUTHOR]