A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images.
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ABSTRACT:
SUBMITTER: Siriapisith T
PROVIDER: S-EPMC8670388 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundHigh quality 3D information of the microscopic plant tissue morphology-the spatial organization of cells and intercellular spaces in tissues-helps in understanding physiological processes in a wide variety of plants and tissues. X-ray micro-CT is a valuable tool that is becoming increasingly available in plant research to obtain 3D microstructural information of the intercellular pore space and individual pore sizes and shapes of tissues. However, individual cell morphology is difficult to retrieve from micro-CT as cells cannot be segmented properly due to negligible density differences at cell-to-cell interfaces. To address this, deep learning-based models were trained and tested to segment individual cells using X-ray micro-CT images of parenchyma tissue samples from apple and pear fruit with different cell and porosity characteristics.ResultsThe best segmentation model achieved an Aggregated Jaccard Index (AJI) of 0.86 and 0.73 for apple and pear tissue, respectively, which is an improvement over the current benchmark method that achieved AJIs of 0.73 and 0.67. Furthermore, the neural network was able to detect other plant tissue structures such as vascular bundles and stone cell clusters (brachysclereids), of which the latter were shown to strongly influence the spatial organization of pear cells. Based on the AJIs, apple tissue was found to be easier to segment, as the porosity and specific surface area of the pore space are higher and lower, respectively, compared to pear tissue. Moreover, samples with lower pore network connectivity, proved very difficult to segment.ConclusionsThe proposed method can be used to automatically quantify 3D cell morphology of plant tissue from micro-CT instead of opting for laborious manual annotations or less accurate segmentation approaches. In case fruit tissue porosity or pore network connectivity is too low or the specific surface area of the pore space too high, native X-ray micro-CT is unable to provide proper marker points of cell outlines, and one should rely on more elaborate contrast-enhancing scan protocols.
| S-EPMC10799452 | biostudies-literature
Project description:With the development of hybrid imaging scanners, micro-CT is widely used in locating abnormalities, studying drug metabolism, and providing structural priors to aid image reconstruction in functional imaging. Due to the low contrast of soft tissues, segmentation of soft tissue organs from mouse micro-CT images is a challenging problem. In this paper, we propose a mouse segmentation scheme based on dynamic contrast enhanced micro-CT images. With a homemade fast scanning micro-CT scanner, dynamic contrast enhanced images were acquired before and after injection of non-ionic iodinated contrast agents (iohexol). Then the feature vector of each voxel was extracted from the signal intensities at different time points. Based on these features, the heart, liver, spleen, lung, and kidney could be classified into different categories and extracted from separate categories by morphological processing. The bone structure was segmented using a thresholding method. Our method was validated on seven BALB/c mice using two different classifiers: a support vector machine classifier with a radial basis function kernel and a random forest classifier. The results were compared to manual segmentation, and the performance was assessed using the Dice similarity coefficient, false positive ratio, and false negative ratio. The results showed high accuracy with the Dice similarity coefficient ranging from 0.709 ± 0.078 for the spleen to 0.929 ± 0.006 for the kidney.
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Project description:In this paper, we propose a framework for CT image segmentation of oil rock core. According to the characteristics of CT image of oil rock core, the existing level set segmentation algorithm is improved. Firstly, an algorithm of Chan-Vese (C-V) model is carried out to segment rock core from image background. Secondly the gray level of image background region is replaced by the average gray level of rock core, so that image background does not affect the binary segmentation. Next, median filtering processing is carried out. Finally, an algorithm of local binary fitting (LBF) model is executed to obtain the crack region. The proposed algorithm has been applied to oil rock core CT images with promising results.
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Project description:Mesoscopic photoacoustic imaging (PAI) enables label-free visualization of vascular networks in tissues with high contrast and resolution. Segmenting these networks from 3D PAI data and interpreting their physiological and pathological significance is crucial yet challenging due to the time-consuming and error-prone nature of current methods. Deep learning offers a potential solution; however, supervised analysis frameworks typically require human-annotated ground-truth labels. To address this, an unsupervised image-to-image translation deep learning model is introduced, the Vessel Segmentation Generative Adversarial Network (VAN-GAN). VAN-GAN integrates synthetic blood vessel networks that closely resemble real-life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D photoacoustic images. Applied to a diverse range of in silico, in vitro, and in vivo data, including patient-derived breast cancer xenograft models and 3D clinical angiograms, VAN-GAN demonstrates its capability to facilitate accurate and unbiased segmentation of 3D vascular networks. By leveraging synthetic data, VAN-GAN reduces the reliance on manual labeling, thus lowering the barrier to entry for high-quality blood vessel segmentation (F1 score: VAN-GAN vs. U-Net = 0.84 vs. 0.87) and enhancing preclinical and clinical research into vascular structure and function.
| S-EPMC11348209 | biostudies-literature
Project description:The segmentation of organs at risk is a crucial and time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and low contrast to surrounding structures, segmenting the parotid gland is challenging. Motivated by the recent success of deep learning, we study the use of two-dimensional (2-D), 2-D ensemble, and three-dimensional (3-D) U-Nets for segmentation. The mean Dice similarity to ground truth is ?0.83 for all three models. A patch-based approach for class balancing seems promising for false-positive reduction. The 2-D ensemble and 3-D U-Net are applied to the test data of the 2015 MICCAI challenge on head and neck autosegmentation. Both deep learning methods generalize well onto independent data (Dice 0.865 and 0.88) and are superior to a selection of model- and atlas-based methods with respect to the Dice coefficient. Since appropriate reference annotations are essential for training but often difficult and expensive to obtain, it is important to know how many samples are needed for training. We evaluate the performance after training with different-sized training sets and observe no significant increase in the Dice coefficient for more than 250 training cases.
| S-EPMC6165912 | biostudies-literature
Project description:ObjectivesColorectal cancer (CRC), the third most common cancer in the USA, is a leading cause of cancer-related death worldwide. Up to 60% of patients develop liver metastasis (CRLM). Treatments like radiation and ablation therapies require disease segmentation for planning and therapy delivery. For ablation, ablation-zone segmentation is required to evaluate disease coverage. We hypothesize that fully convolutional (FC) neural networks, trained using novel methods, will provide rapid and accurate identification and segmentation of CRLM and ablation zones.MethodsFour FC model styles were investigated: Standard 3D-UNet, Residual 3D-UNet, Dense 3D-UNet, and Hybrid-WNet. Models were trained on 92 patients from the liver tumor segmentation (LiTS) challenge. For the evaluation, we acquired 15 patients from the 3D-IRCADb database, 18 patients from our institution (CRLM = 24, ablation-zone = 19), and those submitted to the LiTS challenge (n = 70). Qualitative evaluations of our institutional data were performed by two board-certified radiologists (interventional and diagnostic) and a radiology-trained physician fellow, using a Likert scale of 1-5.ResultsThe most accurate model was the Hybrid-WNet. On a patient-by-patient basis in the 3D-IRCADb dataset, the median (min-max) Dice similarity coefficient (DSC) was 0.73 (0.41-0.88), the median surface distance was 1.75 mm (0.57-7.63 mm), and the number of false positives was 1 (0-4). In the LiTS challenge (n = 70), the global DSC was 0.810. The model sensitivity was 98% (47/48) for sites ≥15 mm in diameter. Qualitatively, 100% (24/24; minority vote) of the CRLM and 84% (16/19; majority vote) of the ablation zones had Likert scores ≥4.ConclusionThe Hybrid-WNet model provided fast (<30 s) and accurate segmentations of CRLM and ablation zones on contrast-enhanced CT scans, with positive physician reviews.
| S-EPMC9403767 | biostudies-literature
Project description:Several studies have focused on the role of epicardial fat in the pathogenesis of cardiovascular disease (CVD). The main purpose of the study was to evaluate a computerized method for the quantitative analysis of epicardial fat volume (EFV) by non-contrast cardiac CT (NCT) for coronary calcium scan and coronary CT angiography (coronary CTA).Thirty patients (61±12.5 years, 73% male, body mass index (BMI) =25.9±6.3 kg/m2) referred to our Institution for suspected coronary artery disease (CAD) underwent NCT and coronary CTA. Epicardial boundaries were traced by 2 experienced operators (operator 1, operators 2) on 3 and 6 short-axis (SA) slices. EFV was computed with a semi-automatic method using an in-house developed software based on spherical harmonic representation of the epicardial surface. In order to analyze the inter-observer variability both the Coefficient of Repeatability (CR) and Intra Class Correlation (ICC) were computed.The total EFV was 103.62±50.97 and 94.96±67.91 cc in NCT and coronary CTA with non-significant difference (P=0.292). CR error was 10.22 cc for operator 1 and 11.31 cc for operator 2 in NCT and 7.99 cc for operator 1 and 7.75 cc for operator 2 in coronary CTA. To analyze the inter-observer variability CR and ICC were computed. CR was 8.17 and 8.39 cc with NCT and 7.07 and 7.21 cc with CTA for 6 and 3 SA slices respectively. ICC values >0.99 were obtained in all cases. The right ventricular EFV was 67.23±31.4 and 57.41±34.3 cc for NCT and coronary CTA respectively; the corresponding values for left ventricular EFV were 38.01±19.1 and 35.27±25.9 cc.Both NCT and coronary CTA can be used with low intra- and inter-observer variability for computer-assisted measurements of EFV. Cardiac CT may allow a fast and reliable computation of EFV in clinical setting.
| S-EPMC5537129 | biostudies-other
Project description:Segmentation of diseased liver remains a challenging task in clinical applications due to the high inter-patient variability in liver shapes, sizes and pathologies caused by cancers or other liver diseases. In this paper, we present a multi-resolution mesh segmentation algorithm for 3D segmentation of livers, called iterative mesh transformation that deforms the mesh of a region-of-interest (ROI) in a progressive manner by iterations between mesh transformation and contour optimization. Mesh transformation deforms the 3D mesh based on the deformation transfer model that searches the optimal mesh based on the affine transformation subjected to a set of constraints of targeting vertices. Besides, contour optimization searches the optimal transversal contours of the ROI by applying the dynamic-programming algorithm to the intersection polylines of the 3D mesh on 2D transversal image planes. The initial constraint set for mesh transformation can be defined by a very small number of targeting vertices, namely landmarks, and progressively updated by adding the targeting vertices selected from the optimal transversal contours calculated in contour optimization. This iterative 3D mesh transformation constrained by 2D optimal transversal contours provides an efficient solution to a progressive approximation of the mesh of the targeting ROI. Based on this iterative mesh transformation algorithm, we developed a semi-automated scheme for segmentation of diseased livers with cancers using as little as five user-identified landmarks. The evaluation study demonstrates that this semi-automated liver segmentation scheme can achieve accurate and reliable segmentation results with significant reduction of interaction time and efforts when dealing with diseased liver cases.
| S-EPMC4450142 | biostudies-literature
Project description:Purpose To compare epicardial fat in patients with arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) with that in healthy subjects. Materials and Methods In this retrospective study, cardiac CT scans in 44 patients with ARVD/C (mean age, 39 years ± 12; 23 men) were compared with those in 45 control group participants between January 2008 and July 2015. Volumes of intrathoracic adipose tissue, mediastinal adipose tissue (MAT), and total epicardial adipose tissue (EAT) were quantified. EAT was subdivided into three regions-right ventricular (RV) EAT, left ventricular (LV) EAT, and peri-atrial EAT (atrial EAT)-and normalized to MAT for all regions. Logistic regression and receiver operating characteristic analysis were performed to evaluate the association between epicardial fat with the diagnosis of ARVD/C. Results Total EAT volume was higher in patients with ARVD/C than in healthy control group participants (median, 98 mL vs 76 mL, respectively; P = .04). Regionally, LV and RV EAT volumes were higher in patients with ARVD/C than in control group participants, most notably when indexed to MAT (median LV EAT index: 0.49 vs 0.15, respectively; median RV EAT index: 0.91 vs 0.52; P ? .0005 for both). The optimal cutoff for diagnosis of ARVD/C was an LV EAT index of 0.24, with a sensitivity and specificity of 91% and 71%, respectively. Atrial EAT volume and total intrathoracic adipose tissue volume were not different between groups. RV diameter showed a positive correlation with total EAT index and LV EAT index (r = 0.21, P = .05 and r = 0.33, P = .002, respectively). Conclusion Higher amounts of right ventricular and left ventricular epicardial fat are found in hearts with arrhythmogenic right ventricular dysplasia/cardiomyopathy, particularly adjacent to the left ventricle, which correlates with disease severity and helps differentiate patients from healthy subjects. © RSNA, 2018 Online supplemental material is available for this article.
| S-EPMC6276062 | biostudies-literature
Project description:Epicardial adipose tissue volume (EAT) has been linked to coronary artery disease and the risk of major adverse cardiac events. As manual quantification of EAT is time-consuming, requires specialized training, and is prone to human error, we developed a deep learning method (DeepFat) for the automatic assessment of EAT on non-contrast low-dose CT calcium score images. Our DeepFat intuitively segmented the tissue enclosed by the pericardial sac on axial slices, using two preprocessing steps. First, we applied a HU-attention-window with a window/level 350/40-HU to draw attention to the sac and reduce numerical errors. Second, we applied a novel look ahead slab-of-slices with bisection ("bisect") in which we split the heart into halves and sequenced the lower half from bottom-to-middle and the upper half from top-to-middle, thereby presenting an always increasing curvature of the sac to the network. EAT volume was obtained by thresholding voxels within the sac in the fat window (- 190/- 30-HU). Compared to manual segmentation, our algorithm gave excellent results with volume Dice = 88.52% ± 3.3, slice Dice = 87.70% ± 7.5, EAT error = 0.5% ± 8.1, and R = 98.52% (p < 0.001). HU-attention-window and bisect improved Dice volume scores by 0.49% and 3.2% absolute, respectively. Variability between analysts was comparable to variability with DeepFat. Results compared favorably to those of previous publications.
| S-EPMC8831577 | biostudies-literature