Vascular Segmentation Algorithms for Generating 3D Atherosclerotic Measurements

Atherosclerosis manifests as plaques within large arteries of the body and remains as a leading cause of mortality and morbidity in the world. Major cardiovascular events may occur in patients without known preexisting symptoms, thus it is important to monitor progression and regression of the plaque burden in the arteries for evaluating patient\u27s response to therapy. In this dissertation, our main focus is quantification of plaque burden from the carotid and femoral arteries, which are major sites for plaque formation, and are straight forward to image noninvasively due to their superficial location. Recently, 3D measurements of plaque burden have shown to be more sensitive to the changes of plaque burden than one-/two-dimensional measurements. However, despite the advancements of 3D noninvasive imaging technology with rapid acquisition capabilities, and the high sensitivity of the 3D plaque measurements of plaque burden, they are still not widely used due to the inordinate amount of time and effort required to delineate artery walls plus plaque boundaries to obtain 3D measurements from the images. Therefore, the objective of this dissertation is developing novel semi-automated segmentation methods to alleviate measurement burden from the observer for segmentation of the outer wall and lumen boundaries from: (1) 3D carotid ultrasound (US) images, (2) 3D carotid black-blood magnetic resonance (MR) images, and (3) 3D femoral black-blood MR images. Segmentation of the carotid lumen and outer wall from 3DUS images is a challenging task due to low image contrast, for which no method has been previously reported. Initially, we developed a 2D slice-wise segmentation algorithm based on the level set method, which was then extended to 3D. The 3D algorithm required fewer user interactions than manual delineation and the 2D method. The algorithm reduced user time by ≈79% (1.72 vs. 8.3 min) compared to manual segmentation for generating 3D-based measurements with high accuracy (Dice similarity coefficient (DSC)\u3e90%). Secondly, we developed a novel 3D multi-region segmentation algorithm, which simultaneously delineates both the carotid lumen and outer wall surfaces from MR images by evolving two coupled surfaces using a convex max-flow-based technique. The algorithm required user interaction only on a single transverse slice of the 3D image for generating 3D surfaces of the lumen and outer wall. The algorithm was parallelized using graphics processing units (GPU) to increase computational speed, thus reducing user time by 93% (0.78 vs. 12 min) compared to manual segmentation. Moreover, the algorithm yielded high accuracy (DSC \u3e 90%) and high precision (intra-observer CV \u3c 5.6% and inter-observer CV \u3c 6.6%). Finally, we developed and validated an algorithm based on convex max-flow formulation to segment the femoral arteries that enforces a tubular shape prior and an inter-surface consistency of the outer wall and lumen to maintain a minimum separation distance between the two surfaces. The algorithm required the observer to choose only about 11 points on its medial axis of the artery to yield the 3D surfaces of the lumen and outer wall, which reduced the operator time by 97% (1.8 vs. 70-80 min) compared to manual segmentation. Furthermore, the proposed algorithm reported DSC greater than 85% and small intra-observer variability (CV ≈ 6.69%). In conclusion, the development of robust semi-automated algorithms for generating 3D measurements of plaque burden may accelerate translation of 3D measurements to clinical trials and subsequently to clinical care

Patch-based convolutional neural network for differentiation of cyst from solid renal mass on contrast-enhanced computed tomography images

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%, 95.64%, and 91.64%. Our method yielded accuracy of 91.21% ± 25.88% as mean ± 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

Evaluation of a T1 mapping technique for stratifying patient risk: A preliminary study using computer simulations of cardiac electrophysiology

Heterogeneous scarred tissue, or infarct, stemming from coronary artery occlusion has been linked to ventricular tachycardia (VT). Myocardial infarct (MI)-based indices derived from contrast enhanced (CE) magnetic resonance have been increasingly investigated to complement the standard risk stratification strategy of patients for implantable cardioverter defibrillator (ICD) therapy. Compared to conventional CE-MRI, measurements derived using Multi-Contrast Late Enhancement (MCLE), a novel T1 mapping technique, have been shown to be more sensitive in predicting appropriate ICD therapy for patients post-MI The objective of this study is to evaluate MCLE for stratifying patient risk using computer simulations of cardiac electrophysiology. A cohort of 25 patients with ischemic cardiomyopathy were imaged using both techniques prior to ICD implantation and were followed up for 6-46 months. Acquired images from both techniques were semi-automatically segmented to create computational models of the heart. These models were then virtually stimulated. The study concluded that MCLE is slightly more reproducible than the conventional CE-MRI. and preliminary results indicated that MCLE showed higher sensitivity and specificity than its counterpart in predicting appropriate ICD therapy

Semi-automated myocardial segmentation in native T1-mapping CMR using deformable non-rigid registration of CINE images

T1-mapping cardiac magnetic resonance (CMR) is a rapidly expanding non-invasive tool for quantitative assessment of myocardial fibrosis. To achieve both efficiency and reproducibility in quantification of T1 measures, automated myocardial boundary tracing is desirable. Accordingly, the application of robust segmentation algorithms for this modality are of significant interest. However, conventional algorithms may fail in myocardial segmentation of T1-mapping images due to low signal gradients at the endocardial-blood pool boundary. In this work, we propose using prior information from cinematic (CINE) CMR images toward accurate myocardial segmentation of native T1-mapping images, acquired using the shortened modified Look-Locker imaging (shMOLLI) technique. We use a three-step framework, which begins with pre-processing and resizing of both CINE and shMOLLI images. Next, we implement semi-automated segmentation of the myocardium on resized CINE images using a deformable model-based technique, via the freely available software Segment v2.2. The final step of our framework is registration and propagation of the CINE contours to corresponding (slice-matched) native shMOLLI images using a non-rigid registration technique based on a modality independent neighborhood descriptor (MIND). We validate our technique on 20 image sets obtained from 20 patients with confirmed myocardial fibrosis related to ischemic injury (myocardial infarction). Our method achieved an average Dice similarity coefficient (DSC) of 84.36% ± 4.03%, precision of 91.68% ± 7.89%, recall of 91.33% ± 8.41% and relative area error of 16.29% ± 8.58%

Computational heart modeling for evaluating efficacy of MRI techniques in predicting appropriate ICD therapy

The objective of this study is to use individualized heart computer models in evaluating efficacy of myocardial infarct (MI) mass determined by two different MRI techniques in predicting patient risk for post-MI ventricular tachycardia (VT). 27 patients with MI underwent late gadolinium-enhanced MRI using inversion-recovery fast gradient echo (IR-FGRE) and multi-contrast late enhancement (MCLE) prior to implantable cardioverter defibrillators (ICD) implantation and were followed up for 6–46 months. The myocardium, MI core (IC), and border zone (BZ) were segmented from the images using previously validated techniques. The segmented structures were then reconstructed as a high-resolution label map in 3D. Individualized image-based computational models were built separately for each imaging technique; simulations of propensity to VT were conducted with each model. The imaging methods were evaluated for sensitivity and specificity by comparing simulated inducibility of VT to clinical outcome (appropriate ICD therapy) in patients. Twelve patients had at least one appropriate ICD therapy for VT at follow-up. For both MCLE and IR-FGRE, the outcomes of the simulations of VT were significantly different between the groups with and without ICD therapy. Between the IR-FGRE and MCLE, the virtual models built using the latter may have yielded higher sensitivity and specificity in predicting appropriate ICD therapy

Automated segmentation of prostate zonal anatomy on T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images using U-Nets

Purpose: Accurate regional segmentation of the prostate boundaries on magnetic resonance (MR) images is a fundamental requirement before automated prostate cancer diagnosis can be achieved. In this paper, we describe a novel methodology to segment prostate whole gland (WG), central gland (CG), and peripheral zone (PZ), where PZ + CG = WG, from T2W and apparent diffusion coefficient (ADC) map prostate MR images. Methods: We designed two similar models each made up of two U-Nets to delineate the WG, CG, and PZ from T2W and ADC map MR images, separately. The U-Net, which is a modified version of a fully convolutional neural network, includes contracting and expanding paths with convolutional, pooling, and upsampling layers. Pooling and upsampling layers help to capture and localize image features with a high spatial consistency. We used a dataset consisting of 225 patients (combining 153 and 72 patients with and without clinically significant prostate cancer) imaged with multiparametric MRI at 3 Tesla. Results and conclusion: Our proposed model for prostate zonal segmentation from T2W was trained and tested using 1154 and 1587 slices of 100 and 125 patients, respectively. Median of Dice similarity coefficient (DSC) on test dataset for prostate WG, CG, and PZ were 95.33 ± 7.77%, 93.75 ± 8.91%, and 86.78 ± 3.72%, respectively. Designed model for regional prostate delineation from ADC map images was trained and validated using 812 and 917 slices from 100 and 125 patients. This model yielded a median DSC of 92.09 ± 8.89%, 89.89 ± 10.69%, and 86.1 ± 9.56% for prostate WG, CG, and PZ on test samples, respectively. Further investigation indicated that the proposed algorithm reported high DSC for prostate WG segmentation from both T2W and ADC map MR images irrespective of WG size. In addition, segmentation accuracy in terms of DSC does not significantly vary among patients with or without significant tumors. Significance: We describe a method for automated prostate zonal segmentation usin

3D scar segmentation from LGE-MRI using a continuous max-flow method

Myocardial scar, a non-viable tissue which forms in the myocardium due to insufficient blood supply to the heart muscle, is one of the leading causes of life-threatening heart disorders, including arrhythmias. Accurate reconstruction of myocardial scar geometry is important for diagnosis and clinicial prognosis of the patients with ischemic cardiomyopathy. The 3D late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is increasingly being investigated for assessing myocardial tissue viability. For applications, such as computational modeling of cardiac electrophysiology aimed at stratifying patient risk for post-infarction arrhythmias, segmentation and reconstruction of the intact geometry of scar is required. However, manual analysis and segmentation of myocardial scar from 3D LGE-MRI is a tedious task. Therefore, semi-automated and fully-automated segmentation algorithms are highly desirable in a clinical setting. In this study, we developed an approach to segment the myocardial scar from 3D LGE-MR images using a continuous max-flow (CMF) method. The data term comprised of a distribution matching term for scar and normal myocardium and a boundary smoothness term for the scar boundaries. The region-of-interest for the scar segmentation is constrained, using manually segmented myocardium. We evaluated our CMF method for accuracy by comparing it to manual scar delineations using 3D LGE-MR images of 34 patients. We compare the results of the CMF technique to ones by conventional full-width-at-half-maximum (FWHM) and signal-threshold-to-reference-mean (STRM) methods. The CMF method yielded a Dice similarity coefficient (DSC) of 72±18% and an absolute volume error (V E) of 15.42±14.1 cm3. Overall, the CMF method outperformed the state-of-the-art methods for all reported metrics in 3D scar segmentation except for the recall value which STRM 2-SD perf

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Background: Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis: To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U-Net-based model. Study Type: Retrospective, case–control. Population: In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. Field Strength: 3T, ADC maps. Sequence: ADC map. Assessment: The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI-radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per-slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t-test was used to test the differences between the performances of constituent networks of the ensemble model. Results: Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. Data Conclusion: An ensemble U-Net-based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. Level of Evidence: 4. Technical Efficacy: Stage 1. J. Magn. Reson. Imaging 2019

Segmentation of Integrated Circuit Layouts from Scan Electron Microscopy Images

One of the most important steps in the extraction of layout for reverse engineering of the integrated circuits (ICs) is the image segmentation of wires and vias from scan electron microscope (SEM) images. This segmentation is challenging due to the gigabytes of image data just for a single IC, image noise, and artefacts. Existing approaches rely on image intensity threshold-based methods but requires significant amount of manual user interactions to correct errors in segmentation. In this paper, we describe an image processing pipeline for segmenting IC layouts from SEM images. Our pipeline includes image normalization, image preprocessing, and segmentation. The segmentation results were compared using a custom-built comparison tool. The results showed, with the correct filters/methods selection, an increase in accuracy of the segmentation for all tested image sets

An ensemble of U-Net architecture variants for left atrial segmentation

Segmentation of the left atrium and proximal pulmonary veins is an important clinical step for diagnosis of atrial fibrillation. However, the automatic segmentation of the left atrium from late gadolinium-enhanced magnetic resonance (LGE-MRI) images remains a challenging task due to differences in acquisition and large variability between individuals. Deep learning has shown to outperform traditional methodologies for segmentation in numerous tasks. A popular deep learning architecture for segmentation is the U-Net, which has shown promising results biomedical segmentation problems. Many newer network architectures have been proposed that leverage the base U-Net architecture such as attention U-Net, dense U-Net and residual U-Net. These models incorporate updated encoder blocks into the U-Net architecture to incrementally improve performance over the base U-Net. Currently, there is no comprehensive evaluation of performance between these models. In this study we (1) explore approaches for the segmentation of the left atrium based on different-Net architectures. (2) We compare and evaluate these on the STACOM 2018 Atrial Segmentation Challenge dataset and (3) ensemble these models to improve overall segmentation by reducing the internal variance between models and architectures. (4) Lastly, we define and build upon a U-Net framework to simplify development of novel U-Net inspired architectures. Our ensemble achieves a mean Dice similarity coefficient (DSC) of 92.1 ± 2.0% on a test set of twenty 3D LGE-MRI images, outperforming other fully automatic segmentation methodologies