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In this paper, we propose a novel global optimization based 3-D multi-region segmentation algorithm for T1-weighted black-blood carotid magnetic resonance (MR) images. The proposed algorithm partitions a 3-D carotid MR image into ...
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In this paper, we propose a novel global optimization based 3-D multi-region segmentation algorithm for T1-weighted black-blood carotid magnetic resonance (MR) images. The proposed algorithm partitions a 3-D carotid MR image into three regions: wall, lumen, and background. The algorithm performs such partitioning by simultaneously evolving two coupled 3-D surfaces of carotid artery adventitia boundary (AB) and lumen-intima boundary (LIB) while preserving their anatomical inter-surface consistency such that the LIB is always located within the AB. In particular, we show that the proposed algorithm results in a fully time implicit scheme that propagates the two linearly ordered surfaces of the AB and LIB to their globally optimal positions during each discrete time frame by convex relaxation. In this regard, we introduce the continuous max-flow model and prove its duality/equivalence to the convex relaxed optimization problem with respect to each evolution step. We then propose a fully parallelized continuous max-flow-based algorithm, which can be readily implemented on a GPU to achieve high computational efficiency. Extensive experiments, with four users using 12 3T MR and 26 1.5T MR images, demonstrate that the proposed algorithm yields high accuracy and low operator variability in computing vessel wall volume. In addition, we show the algorithm outperforms previous methods in terms of high computational efficiency and robustness with fewer user interactions.
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We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graph...
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We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graphical user interface to initialize a fast max-flow-based segmentation algorithm and segment scar accurately with progressive interaction. We propose a partially-ordered Potts (POP) model to multi-region segmentation to properly encode the known spatial consistency of cardiac regions. Its generalization introduces a custom label/region order constraint to Potts model to multi-region segmentation. The combinatorial optimization problem associated with the proposed POP model is solved by means of convex relaxation, for which a novel multi-level continuous max-flow formulation, i.e., the hierarchical continuous max-flow (HMF) model, is proposed and studied. We demonstrate that the proposed HMF model is dual or equivalent to the convex relaxed POP model and introduces a new and efficient hierarchical continuous max-flow based algorithm by modern convex optimization theory. In practice, the introduced hierarchical continuous max-flow based algorithm can be implemented on the parallel GPU to achieve significant acceleration in numerics. Experiments are performed in 50 whole heart 3-D LE datasets, 35 with left-ventricular and 15 with right-ventricular scar. The experimental results are compared to full-width-at-half-maximum and Signal-threshold to reference-mean methods using manual expert myocardial segmentations and operator variabilities and the effect of user interaction are assessed. The results indicate a substantial reduction in image processing time with robust accuracy for detection of myocardial scar. This is achieved without the need for additional region constraints and using a single optimization procedure, substantially reducing the potential for error.
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Purpose: Manual analysis of clinical placenta pathology samples under the microscope is a costly and time-consuming task. Computer-aided diagnosis might offer a means to obtain fast and reliable results and also substantially redu...
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Purpose: Manual analysis of clinical placenta pathology samples under the microscope is a costly and time-consuming task. Computer-aided diagnosis might offer a means to obtain fast and reliable results and also substantially reduce inter- and intra-rater variability. Here, we present a fully automated segmentation method that is capable of distinguishing the complex histological features of the human placenta (i. e., the chorionic villous structures).
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Automated classification of renal masses detected at computed tomography (CT) examinations into benign cyst versus solid mass is clinically valuable. This distinction may be challenging at single-phase contrast-enhanced CE-CT exam...
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Automated classification of renal masses detected at computed tomography (CT) examinations into benign cyst versus solid mass is clinically valuable. This distinction may be challenging at single-phase contrast-enhanced CE-CT examinations, where cysts may simulate solid masses and where renal masses are most commonly incidentally detected. This may lead to unnecessary and costly follow-up imaging for accurate characterization. In this paper, we describe a patch-based CNN method to differentiate benign cysts from solid renal masses using single-phase CECT images. The predictions of the network for patches extracted from a manually segmented lesion are combined through the majority voting system for final diagnosis. We used a dataset comprised of single-phase CECT images of 315 patients with 77 benign (oncocytomas, and fat poor renal angiomyolipoma) and 238 malignant (renal cell carcinoma including clear cell, papillary, and chromophobe subtypes) tumors. We trained our proposed network using patches extracted and artificially augmented from 40 CECT scans. The presented algorithm was evaluated using 275 unseen CECT test images consisting of 327 renal masses by comparing algorithm-generated labels to those labeled by experts and achieved mean accuracy, precision, and recall of 88.96 & x0025;, 95.64 & x0025;, and 91.64 & x0025;. Our method yielded accuracy of 91.21 & x0025; & x00B1; 25.88 & x0025; as mean & x00B1; standard deviation at the patient level. The AUC was reported as 0.804. The results indicate that our algorithm may accurately characterize benign cysts from solid masses with a high degree of accuracy and may be clinically valuable to prevent unnecessary imaging follow-up for characterization in a proportion of patients.
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