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: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:Photon-counting micro computed tomography (micro-CT) offers new potential in preclinical imaging, particularly in distinguishing materials. It becomes especially helpful when combined with contrast agents, enabling the differentiation of tumors from surrounding tissues. There are mainly two types of contrast agents in the market for micro-CT: small molecule-based and nanoparticle-based. However, despite their widespread use in liver tumor studies, there is a notable gap in research on the application of these commercially available agents for photon-counting micro-CT in breast and ovarian tumors. Herein, we explored the effectiveness of these agents in differentiating tumor xenografts from various origins (AU565, MDA-MB-231, and SKOV-3) in nude mice, using photon-counting micro-CT. Specifically, ISOVUE-370 (a small molecule-based agent) and Exitrone Nano 12000 (a nanoparticle-based agent) were investigated in this context. To improve tumor visualization, we proposed a novel color visualization method for photon-counting micro-CT, which changes color tones to highlight contrast media distribution, offering a robust alternative to traditional material decomposition methods with less computational demand. Our in vivo experiments confirm its effectiveness, showing distinct enhancement characteristics for each contrast agent. Qualitative and quantitative analyses suggested that Exitrone Nano 12000 provides superior vasculature enhancement and better quantitative consistency across scans, while ISOVUE-370 gives more comprehensive tumor enhancement but with a significant variance between scans due to its short blood half-time. This variability leads to high sensitivity to timing and individual differences among mice. Further, a paired t-test on mean and standard deviation values within tumor volumes showed significant differences between the AU565 and SKOV-3 tumor models with the nanoparticle-based (p-values < 0.02), attributable to their distinct vascularity, as confirmed by immunohistochemistry. These findings underscore the utility of photon-counting micro-CT in non-invasively assessing the morphology and anatomy of different tumor xenografts, which is crucial for tumor characterization and longitudinal monitoring of tumor development and response to treatments.
| S-EPMC10802390 | biostudies-literature
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: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: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:This paper investigates the effect of anisotropic resolution on the image textural features of pharmacokinetic (PK) parameters of a murine glioma model using dynamic contrast-enhanced (DCE) MR images acquired with an isotropic resolution at 7T with pre-contrast T1 mapping. The PK parameter maps of whole tumors at isotropic resolution were generated using the two-compartment exchange model combined with the three-site-two-exchange model. The textural features of these isotropic images were compared with those of simulated, thick-slice, anisotropic images to assess the influence of anisotropic voxel resolution on the textural features of tumors. The isotropic images and parameter maps captured distributions of high pixel intensity that were absent in the corresponding anisotropic images with thick slices. A significant difference was observed in 33% of the histogram and textural features extracted from anisotropic images and parameter maps, compared to those extracted from corresponding isotropic images. Anisotropic images in different orthogonal orientations demonstrated 42.1% of the histogram and textural features to be significantly different from those of isotropic images. This study demonstrates that the anisotropy of voxel resolution needs to be carefully considered when comparing the textual features of tumor PK parameters and contrast-enhanced images.
| S-EPMC10141208 | biostudies-literature
Project description:For image-guided small animal irradiations, the whole workflow of imaging, organ contouring, irradiation planning, and delivery is typically performed in a single session requiring continuous administration of anaesthetic agents. Automating contouring leads to a faster workflow, which limits exposure to anaesthesia and thereby, reducing its impact on experimental results and on animal wellbeing. Here, we trained the 2D and 3D U-Net architectures of no-new-Net (nnU-Net) for autocontouring of the thorax in mouse micro-CT images. We trained the models only on native CTs and evaluated their performance using an independent testing dataset (i.e., native CTs not included in the training and validation). Unlike previous studies, we also tested the model performance on an external dataset (i.e., contrast-enhanced CTs) to see how well they predict on CTs completely different from what they were trained on. We also assessed the interobserver variability using the generalized conformity index ([Formula: see text]) among three observers, providing a stronger human baseline for evaluating automated contours than previous studies. Lastly, we showed the benefit on the contouring time compared to manual contouring. The results show that 3D models of nnU-Net achieve superior segmentation accuracy and are more robust to unseen data than 2D models. For all target organs, the mean surface distance (MSD) and the Hausdorff distance (95p HD) of the best performing model for this task (nnU-Net 3d_fullres) are within 0.16 mm and 0.60 mm, respectively. These values are below the minimum required contouring accuracy of 1 mm for small animal irradiations, and improve significantly upon state-of-the-art 2D U-Net-based AIMOS method. Moreover, the conformity indices of the 3d_fullres model also compare favourably to the interobserver variability for all target organs, whereas the 2D models perform poorly in this regard. Importantly, the 3d_fullres model offers 98% reduction in contouring time.
| S-EPMC8810936 | 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