A Novel Mouse Segmentation Method Based on Dynamic Contrast Enhanced Micro-CT Images.
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ABSTRACT:
SUBMITTER: Yan D
PROVIDER: S-EPMC5217965 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Speckle variation induced by intracellular motion (IM) in the urothelium was observed in optical coherence tomography (OCT) images. IM can be used as a dynamic contrast to segment the urothelium by comparing two sequential OCT images. This method opens the possibility of specifically tracking the distribution of urothelial cancerous cells for identifying the microinvasion of bladder tumors. OCT images were acquired ex vivo with fresh porcine bladder tissue. IM was analyzed by tracking speckle variation using autocorrelation function, then quantified with constrained regularization method for inverting data (CONTIN method) to identify the decorrelation time (DT) of the speckle variations. Variance analysis was also conducted to show IM amplitude and distribution in the urothelium. The segmentation of the urothelium was demonstrated with OCT images with a visible urothelial layer and OCT images with an invisible urothelial layer. Significant speckle variation induced by IM was observed in the urothelium. However, the distribution of the IM is heterogeneous. The DTs are mostly concentrated between 1 and 30 ms. With the IM as a dynamic contrast, the urothelium can be accurately and exclusively segmented, even the urothelial layer is invisible in normal OCT images. IM can be used as a dynamic contrast to exclusively track urothelial cell distribution. This contrast may provide a new mechanism for OCT to image the invasion depth and pattern of urothelial cancerous cells for accurately substaging of bladder cancer.
| S-EPMC8363479 | biostudies-literature
Project description:PURPOSE:In this study, a 3D fat-based deformable registration algorithm was developed for registering dynamic contrast-enhanced breast images. METHODS:The mutual information similarity measure with free-form deformation motion correction in rapidly enhancing lesions can introduce motion. However, in Dixon-based fat-water separated acquisitions, the nonenhancing fat signal can directly be used to estimate deformable motion, which can be later used to deform the water images. Qualitative comparison of the fat-based registration method to a water-based registration method, and to the unregistered images, was performed by two experienced readers. Quantitative analysis of the registration was evaluated by estimating the mean-squared signal difference on the fat images. RESULTS:Using a scale of 0 (no motion) to 2 (?>?4 voxels of motion), the average image quality score of the fat-based registered images was 0.5?±?0.6, water-based registration was 0.8?±?0.8, and the unregistered dataset was 1.6?±?0.6. The mean-squared-signal-difference metric on the fat images was significantly lower for fat-based registered images compared with both water-based registered and unregistered images. CONCLUSIONS:Fat-based registration of breast dynamic contrast-enhanced images is a promising technique for performing deformable motion correction of breast without introducing new motion. Magn Reson Med 79:2408-2414, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
| S-EPMC6063085 | biostudies-literature
Project description:Epicardial fat (ECF) is localized fat surrounding the heart muscle or myocardium and enclosed by the thin-layer pericardium membrane. Segmenting the ECF is one of the most difficult medical image segmentation tasks. Since the epicardial fat is infiltrated into the groove between cardiac chambers and is contiguous with cardiac muscle, segmentation requires location and voxel intensity. Recently, deep learning methods have been effectively used to solve medical image segmentation problems in several domains with state-of-the-art performance. This paper presents a novel approach to 3D segmentation of ECF by integrating attention gates and deep supervision into the 3D U-Net deep learning architecture. The proposed method shows significant improvement of the segmentation performance, when compared with standard 3D U-Net. The experiments show excellent performance on non-contrast CT datasets with average Dice scores of 90.06%. Transfer learning from a pre-trained model of a non-contrast CT to contrast-enhanced CT dataset was also performed. The segmentation accuracy on the contrast-enhanced CT dataset achieved a Dice score of 88.16%.
| S-EPMC8670388 | biostudies-literature
Project description:Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.
| S-EPMC10571741 | biostudies-literature
Project description:Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as wavelet-based features, both extracted from pixel-based time-signal intensity curves to segment prostate lesions on prostate DCE-MRI.Quantitative dynamic contrast-enhanced MRI data were acquired on 22 patients. Optimal features selected by forward selection are used for the segmentation of prostate lesions by applying fuzzy c-means (FCM) clustering. The images were reviewed by an expert radiologist and manual segmentation performed as the ground truth.Empirical results indicate that fuzzy c-mean classifier can achieve better results in terms of sensitivity, speci?city when semi-quantitative features were considered versus wavelet kinetic features for lesion segmentation (Sensitivity of 87.58% and 75.62%, respectively) and (Specificity of 89.85% and 68.89 %, respectively).The proposed segmentation algorithm in this work can potentially be implemented for automatic prostate lesion detection in a computer aided diagnosis scheme and combined with morphologic features to increase diagnostic credibility.
| S-EPMC5928300 | biostudies-literature
Project description:PURPOSE:Manual delineation of head and neck (H&N) organ-at-risk (OAR) structures for radiation therapy planning is time consuming and highly variable. Therefore, we developed a dynamic multiatlas selection-based approach for fast and reproducible segmentation. METHODS:Our approach dynamically selects and weights the appropriate number of atlases for weighted label fusion and generates segmentations and consensus maps indicating voxel-wise agreement between different atlases. Atlases were selected for a target as those exceeding an alignment weight called dynamic atlas attention index. Alignment weights were computed at the image level and called global weighted voting (GWV) or at the structure level and called structure weighted voting (SWV) by using a normalized metric computed as the sum of squared distances of computed tomography (CT)-radiodensity and modality-independent neighborhood descriptors (extracting edge information). Performance comparisons were performed using 77 H&N CT images from an internal Memorial Sloan-Kettering Cancer Center dataset (N = 45) and an external dataset (N = 32) using Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of HD, median of maximum surface distance, and volume ratio error against expert delineation. Pairwise DSC accuracy comparisons of proposed (GWV, SWV) vs single best atlas (BA) or majority voting (MV) methods were performed using Wilcoxon rank-sum tests. RESULTS:Both SWV and GWV methods produced significantly better segmentation accuracy than BA (P < 0.001) and MV (P < 0.001) for all OARs within both datasets. SWV generated the most accurate segmentations with DSC of: 0.88 for oral cavity, 0.85 for mandible, 0.84 for cord, 0.76 for brainstem and parotids, 0.71 for larynx, and 0.60 for submandibular glands. SWV's accuracy exceeded GWV's for submandibular glands (DSC = 0.60 vs 0.52, P = 0.019). CONCLUSIONS:The contributed SWV and GWV methods generated more accurate automated segmentations than the other two multiatlas-based segmentation techniques. The consensus maps could be combined with segmentations to visualize voxel-wise consensus between atlases within OARs during manual review.
| S-EPMC7042892 | biostudies-literature
Project description:OBJECTIVES: To examine whether dynamic contrast-enhanced CT (DCE-CT) could be used to characterise and safely distinguish between malignant and benign lung tumours in patients with suspected lung cancer. METHODS: Using a quantitative approach to DCE-CT, two separate sets of regions of interest (ROIs) in tissues were placed in each tumour: large ROIs over the entire tumour and small ROIs over the maximally perfused parts of the tumour. Using mathematical modelling techniques and dedicated perfusion software, this yielded a plethora of results. RESULTS: First, because of their non-normal distribution, DCE-CT measurements must be analysed using log scale data transformation. Second, there were highly significant differences between large ROI and small ROI measurements (p<0.001). Thus, the ROI method used in a given study should always be specified in advance. Third, neither quantitative parameters (blood flow and blood volume) nor semi-quantitative parameters (peak enhancement) could be used to distinguish between malignant and benign tumours. This was irrespective of the method of quantification used for large ROIs (0.13<p<0.76) and small ROIs (0.084<p<0.31). Fourth, although there were no indications of systematic reproducibility bias, the 95% limits of agreement were so broad that the risk of disagreement between the measurements could affect the clinical use of the measurements. This lack of reproducibility should be addressed. CONCLUSION AND ADVANCES IN KNOWLEDGE: A quantitative approach to DCE-CT is not a clinically usable method for characterising lung tumours.
| S-EPMC3830431 | biostudies-other
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:ObjectivesTo develop and evaluate a fully automatic method to measure diameters of the ascending and descending aorta on non-ECG-gated, non-contrast computed tomography (CT) scans.Material and methodsThe method combines multi-atlas registration to obtain seed points, aorta centerline extraction, and an optimal surface segmentation approach to extract the aorta surface around the centerline. From the extracted 3D aorta segmentation, the diameter of the ascending and descending aorta was calculated at cross-sectional slices perpendicular to the extracted centerline, at the level of the pulmonary artery bifurcation, and at 1-cm intervals up to 3 cm above and below this level. Agreement with manual annotations was evaluated by dice similarity coefficient (DSC) for segmentation overlap, mean surface distance (MSD), and intra-class correlation (ICC) of diameters on 100 CT scans from a lung cancer screening trial. Repeatability of the diameter measurements was evaluated on 617 baseline-one year follow-up CT scan pairs.ResultsThe agreement between manual and automatic segmentations was good with 0.95 ± 0.01 DSC and 0.56 ± 0.08 mm MSD. ICC between the diameters derived from manual and from automatic segmentations was 0.97, with the per-level ICC ranging from 0.87 to 0.94. An ICC of 0.98 for all measurements and per-level ICC ranging from 0.91 to 0.96 were obtained for repeatability.ConclusionThis fully automatic method can assess diameters in the thoracic aorta reliably even in non-ECG-gated, non-contrast CT scans. This could be a promising tool to assess aorta dilatation in screening and in clinical practice.Key points• Fully automatic method to assess thoracic aorta diameters. • High agreement between fully automatic method and manual segmentations. • Method is suitable for non-ECG-gated CT and can therefore be used in screening.
| S-EPMC6682850 | biostudies-literature
Project description:ObjectivesTo develop a deep learning-based pulmonary vessel segmentation algorithm (DLVS) from noncontrast chest CT and to investigate its clinical implications in assessing vascular remodeling of chronic obstructive lung disease (COPD) patients.MethodsFor development, 104 pulmonary CT angiography scans (49,054 slices) using a dual-source CT were collected, and spatiotemporally matched virtual noncontrast and 50-keV images were generated. Vessel maps were extracted from the 50-keV images. The 3-dimensional U-Net-based DLVS was trained to segment pulmonary vessels (with a vessel map as the output) from virtual noncontrast images (as the input). For external validation, vendor-independent noncontrast CT images (n = 14) and the VESSEL 12 challenge open dataset (n = 3) were used. For each case, 200 points were selected including 20 intra-lesional points, and the probability value for each point was extracted. For clinical validation, we included 281 COPD patients with low-dose noncontrast CTs. The DLVS-calculated volume of vessels with a cross-sectional area < 5 mm2 (PVV5) and the PVV5 divided by total vessel volume (%PVV5) were measured.ResultsDLVS correctly segmented 99.1% of the intravascular points (1,387/1,400) and 93.1% of the extravascular points (1,309/1,400). The areas-under-the receiver-operating characteristic curve (AUROCs) were 0.977 and 0.969 for the two external validation datasets. For the COPD patients, both PPV5 and %PPV5 successfully differentiated severe patients whose FEV1 < 50 (AUROCs; 0.715 and 0.804) and were significantly correlated with the emphysema index (Ps < .05).ConclusionsDLVS successfully segmented pulmonary vessels on noncontrast chest CT by utilizing spatiotemporally matched 50-keV images from a dual-source CT scanner and showed promising clinical applicability in COPD.Key points• We developed a deep learning pulmonary vessel segmentation algorithm using virtual noncontrast images and 50-keV enhanced images produced by a dual-source CT scanner. • Our algorithm successfully segmented vessels on diseased lungs. • Our algorithm showed promising results in assessing the loss of small vessel density in COPD patients.
| S-EPMC8131193 | biostudies-literature