Difuzně vážené zobrazení tkání je zásadní součástí vyšetřovacích protokolů magnetické rezonance. Snížení jeho kvality na 3T přístrojích v porovnání s 1, 5T je při použití konvenčních metod náběru dat významné a vyžaduje hledání nových postupů. Technika RESOLVE (REeadout Segmentation Of Long Variable Echo-trains) představuje volbu, která zmenšuje geometrické distorze a poskytuje vyšší prostorové rozlišení obrazu, nicméně při prodloužené době vyšetření.

Diffusion-weighted tissue imaging is very important part of magnetic resonance examination protocols. There is significant decrease of its quality in 3T compared to 1,5T machines if conventional techniques of data collections are used, and new methods are needed to be applied. RESOLVE (REadout Segmentation Of Long Variable Echo-trains) technique represents an option reducing geometric distortions and providing higher spatial resolution, however, with prolonged examination duration.

• Keywords
• technika RESOLVE,
• MeSH
• Diffusion Magnetic Resonance Imaging * methods   MeSH
• Echo-Planar Imaging methods   MeSH
• Humans MeSH
• Tissues MeSH
• Image Enhancement methods   MeSH
• Diffusion Tensor Imaging * methods   MeSH
• Imaging, Three-Dimensional MeSH
• Check Tag
• Humans MeSH
• Publication type
• Review MeSH

In recent years, computed tomography (CT) has become a standard technique in cardiac imaging because it provides detailed information that may facilitate the diagnosis of the conditions that interfere with correct heart function. However, CT-based cardiac diagnosis requires manual segmentation of heart cavities, which is a difficult and time-consuming task. Thus, in this paper, we propose a novel technique to segment endocardium and epicardium boundaries based on a 2D approach. The proposal computes relevant information of the left ventricle and its adjacent structures using the Hermite transform. The novelty of the work is that the information is combined with active shape models and level sets to improve the segmentation. Our database consists of mid-third slices selected from 28 volumes manually segmented by expert physicians. The segmentation is assessed using Dice coefficient and Hausdorff distance. In addition, we introduce a novel metric called Ray Feature error to evaluate our method. The results show that the proposal accurately discriminates cardiac tissue. Thus, it may be a useful tool for supporting heart disease diagnosis and tailoring treatments.

• MeSH
• Models, Biological MeSH
• Humans MeSH
• Tomography, X-Ray Computed methods   MeSH
• Heart Ventricles pathology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Decellularized tissue is an important source for biological tissue engineering. Evaluation of the quality of decellularized tissue is performed using scanned images of hematoxylin-eosin stained (H&E) tissue sections and is usually dependent on the observer. The first step in creating a tool for the assessment of the quality of the liver scaffold without observer bias is the automatic segmentation of the whole slide image into three classes: the background, intralobular area, and extralobular area. Such segmentation enables to perform the texture analysis in the intralobular area of the liver scaffold, which is crucial part in the recellularization procedure. Existing semi-automatic methods for general segmentation (i.e., thresholding, watershed, etc.) do not meet the quality requirements. Moreover, there are no methods available to solve this task automatically. Given the low amount of training data, we proposed a two-stage method. The first stage is based on classification of simple hand-crafted descriptors of the pixels and their neighborhoods. This method is trained on partially annotated data. Its outputs are used for training of the second-stage approach, which is based on a convolutional neural network (CNN). Our architecture inspired by U-Net reaches very promising results, despite a very low amount of the training data. We provide qualitative and quantitative data for both stages. With the best training setup, we reach 90.70% recognition accuracy.

• MeSH
• Liver * diagnostic imaging   MeSH
• Neural Networks, Computer MeSH
• Image Processing, Computer-Assisted * MeSH
• Semantics * MeSH
• Publication type
• Letter MeSH

• MeSH
• Chordata MeSH
• Embryonic Development MeSH
• Epigenesis, Genetic MeSH
• Phylogeny MeSH
• Head embryology   MeSH
• Mesoderm MeSH
• Somites MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Biologické vědy
• NML Fields
• biologie
• biologie

Diabetic retinopathy is a diabetes complication that affects the eyes, caused by damage to the blood vessels of the light-sensitive tissue of the retina. At the onset, diabetic retinopathy may cause no symptoms or only mild vision problems, but eventually it can cause blindness. Totally automated segmentation of Eye Fundus Images (EFI) is a necessary step for accurate and early quantification of lesions, useful in the future for better automated diagnosis of degree of diabetic retinopathy and damage caused by the disease. Deep Learning segmentation networks are the state-of-the-art, but quality, limitations and comparison of architectures of segmentation networks is necessary. We build off-theshelf deep learning architectures and evaluate them on a publicly available dataset, to conclude the strengths and limitations of the approaches and to compare architectures. Results show that the segmentation networks score high on important metrics, such as 87.5% weighted IoU on FCN. We also show that network architecture is very important, with DeepLabV3 and FCN outperforming other networks tested by more than 30 pp. We also show that DeepLabV3 outperforms prior related work using deep learning to detect lesions. Finally, we identify and investigate the problem of very low IoU and precision scores, such as 17% IoU of microaneurisms in DeepLabV3, concluding it is due to a large number of false positives. This leads us to discuss the challenges that lie ahead to improve the limitations that we identified

• MeSH
• Deep Learning MeSH
• Diabetic Retinopathy * diagnosis   MeSH
• Diagnostic Techniques, Ophthalmological MeSH
• Diagnosis, Computer-Assisted methods   MeSH
• Evaluation Studies as Topic MeSH
• Humans MeSH
• Neural Networks, Computer MeSH
• Check Tag
• Humans MeSH

BACKGROUND: Segmentation of pre-operative low-grade gliomas (LGGs) from magnetic resonance imaging is a crucial step for studying imaging biomarkers. However, segmentation of LGGs is particularly challenging because they rarely enhance after gadolinium administration. Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images. METHODS: First, the user draws a region-of-interest (ROI) that completely encloses the tumor and some normal tissue. Second, a normal brain atlas and post-contrast T1W images are registered to T2W images. Third, the posterior probability of each pixel/voxel belonging to normal and abnormal tissues is calculated based on information derived from the atlas and ROI. Finally, geodesic active contours use the probability map of the tumor to shrink the ROI until optimal tumor boundaries are found. This method was validated against the true segmentation (TS) of 30 LGG patients for both 2D (1 slice) and 3D. The TS was obtained from manual segmentations of three experts using the Simultaneous Truth and Performance Level Estimation (STAPLE) software. Dice and Jaccard indices and other descriptive statistics were computed for the proposed method, as well as the experts' segmentation versus the TS. We also tested the method with the BraTS datasets, which supply expert segmentations. RESULTS AND DISCUSSION: For 2D segmentation vs. TS, the mean Dice index was 0.90 ± 0.06 (standard deviation), sensitivity was 0.92, and specificity was 0.99. For 3D segmentation vs. TS, the mean Dice index was 0.89 ± 0.06, sensitivity was 0.91, and specificity was 0.99. The automated results are comparable with the experts' manual segmentation results. CONCLUSIONS: We present an accurate, robust, efficient, and reproducible segmentation method for pre-operative LGGs.

• MeSH
• Algorithms MeSH
• Glioma pathology   surgery   MeSH
• Humans MeSH
• Magnetic Resonance Imaging * methods   MeSH
• Brain Neoplasms pathology   surgery   MeSH
• Image Processing, Computer-Assisted * MeSH
• Sensitivity and Specificity MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

• MeSH
• Biomechanical Phenomena MeSH
• Diagnostic Imaging * methods   utilization   MeSH
• Humans MeSH
• Mechanical Phenomena MeSH
• Tomography, X-Ray Computed methods   utilization   MeSH
• Image Processing, Computer-Assisted methods   utilization   MeSH
• Visible Human Projects MeSH
• Statistics as Topic MeSH
• Models, Theoretical * MeSH
• Tissues physiology   MeSH
• Imaging, Three-Dimensional * methods   utilization   MeSH
• Check Tag
• Humans MeSH

BACKGROUND: Segmentation of the gray and white matter (GM, WM) of the human spinal cord in MRI images as well as the analysis of spinal cord diffusivity are challenging. When appropriately segmented, diffusion tensor imaging (DTI) of the spinal cord might be beneficial in the diagnosis and prognosis of several diseases. PURPOSE: To evaluate the applicability of a semiautomatic algorithm provided by ITK-SNAP in classification mode (CLASS) for segmenting cervical spinal cord GM, WM in MRI images and analyzing DTI parameters. STUDY TYPE: Prospective. SUBJECTS: Twenty healthy volunteers. SEQUENCES: 1.5T, turbo spin echo, fast field echo, single-shot echo planar imaging. ASSESSMENT: Three raters segmented the tissues by manual, CLASS, and atlas-based methods (Spinal Cord Toolbox, SCT) on T2 -weighted and DTI images. Masks were quantified by similarity and distance metrics, then analyzed for repeatability and mutual comparability. Masks created over T2 images were registered into diffusion space and fractional anisotropy (FA) values were statistically evaluated for dependency on method, rater, or tissue. STATISTICAL TESTS: t-test, analysis of variance (ANOVA), coefficient of variation, Dice coefficient, Hausdorff distance. RESULTS: CLASS segmentation reached better agreement with manual segmentation than did SCT (P < 0.001). Intra- and interobserver repeatability of SCT was better for GM and WM (both P < 0.001) but comparable with CLASS in entire spinal cord segmentation (P = 0.17 and P = 0.07, respectively). While FA values of whole spinal cord were not influenced by choice of segmentation method, both semiautomatic methods yielded lower FA values (P < 0.005) for GM than did the manual technique (mean differences 0.02 and 0.04 for SCT and CLASS, respectively). Repeatability of FA values for all methods was sufficient, with mostly less than 2% variance. DATA CONCLUSION: The presented semiautomatic method in combination with the proposed approach to data registration and analyses of spinal cord diffusivity can potentially be used as an alternative to atlas-based segmentation. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1217-1227.

• MeSH
• Algorithms MeSH
• Anisotropy MeSH
• White Matter diagnostic imaging   MeSH
• Diffusion Magnetic Resonance Imaging * MeSH
• Adult MeSH
• Echo-Planar Imaging * MeSH
• Cervical Cord diagnostic imaging   MeSH
• Humans MeSH
• Young Adult MeSH
• Observer Variation MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Spinal Cord Injuries diagnostic imaging   MeSH
• Prospective Studies MeSH
• Gray Matter diagnostic imaging   MeSH
• Machine Learning MeSH
• Diffusion Tensor Imaging * MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Tissue imaging in 3D using visible light is limited and various clearing techniques were developed to increase imaging depth, but none provides universal solution for all tissues at all developmental stages. In this review, we focus on different tissue clearing methods for 3D imaging of heart and vasculature, based on chemical composition (solvent-based, simple immersion, hyperhydration, and hydrogel embedding techniques). We discuss in detail compatibility of various tissue clearing techniques with visualization methods: fluorescence preservation, immunohistochemistry, nuclear staining, and fluorescent dyes vascular perfusion. We also discuss myocardium visualization using autofluorescence, tissue shrinking, and expansion. Then we overview imaging methods used to study cardiovascular system and live imaging. We discuss heart and vessels segmentation methods and image analysis. The review covers the whole process of cardiovascular system 3D imaging, starting from tissue clearing and its compatibility with various visualization methods to the types of imaging methods and resulting image analysis.

• Publication type
• Journal Article MeSH
• Review MeSH

The complex shape of embryonic cartilage represents a true challenge for phenotyping and basic understanding of skeletal development. X-ray computed microtomography (μCT) enables inspecting relevant tissues in all three dimensions; however, most 3D models are still created by manual segmentation, which is a time-consuming and tedious task. In this work, we utilised a convolutional neural network (CNN) to automatically segment the most complex cartilaginous system represented by the developing nasal capsule. The main challenges of this task stem from the large size of the image data (over a thousand pixels in each dimension) and a relatively small training database, including genetically modified mouse embryos, where the phenotype of the analysed structures differs from the norm. We propose a CNN-based segmentation model optimised for the large image size that we trained using a unique manually annotated database. The segmentation model was able to segment the cartilaginous nasal capsule with a median accuracy of 84.44% (Dice coefficient). The time necessary for segmentation of new samples shortened from approximately 8 h needed for manual segmentation to mere 130 s per sample. This will greatly accelerate the throughput of μCT analysis of cartilaginous skeletal elements in animal models of developmental diseases.

• MeSH
• Cartilage diagnostic imaging   MeSH
• Deep Learning * MeSH
• Mice MeSH
• Neural Networks, Computer MeSH
• Image Processing, Computer-Assisted methods   MeSH
• X-Rays MeSH
• Developmental Biology MeSH
• Animals MeSH
• Check Tag
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH