Eranga Ukwatta

Prostate Segmentation: An Efficient Convex Optimization Approach With Axial Symmetry Using 3-D TRUS and MR Images

We propose a novel global optimization-based approach to segmentation of 3-D prostate transrectal ultrasound (TRUS) and T2 weighted magnetic resonance (MR) images, enforcing inherent axial symmetry of prostate shapes to simultaneously adjust a series of 2-D slice-wise segmentations in a “global” 3-D sense. We show that the introduced challenging combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In this regard, we propose a novel coherent continuous max-flow model (CCMFM), which derives a new and efficient duality-based algorithm, leading to a GPU-based implementation to achieve high computational speeds. Experiments with 25 3-D TRUS images and 30 3-D T2w MR images from our dataset, and 50 3-D T2w MR images from a public dataset, demonstrate that the proposed approach can segment a 3-D prostate TRUS/MR image within 5-6 s including 4-5 s for initialization, yielding a mean Dice similarity coefficient of 93.2% ± 2.0% for 3-D TRUS images and 88.5% ± 3.5% for 3-D MR images. The proposed method also yields relatively low intra- and inter-observer variability introduced by user manual initialization, suggesting a high reproducibility, independent of observers.

Three‐dimensional ultrasound of carotid atherosclerosis: Semiautomated segmentation using a level set‐based method

PURPOSE Three-dimensional ultrasound (3D US) of the carotid artery provides measurements of arterial wall and plaque [vessel wall volume (VWV)] that are complementary to the one-dimensional measurement of the carotid artery intima-media thickness. 3D US VWV requires an observer to delineate the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) of the carotid artery. The main purpose of this work was to develop and evaluate a semiautomated segmentation algorithm for delineating the MAB and LIB of the carotid artery from 3D US images. METHODS To segment the MAB and LIB, the authors used a level set method and combined several low-level image cues with high-level domain knowledge and limited user interaction. First, the operator initialized the algorithm by choosing anchor points on the boundaries, identified in the images. The MAB was segmented using local region- and edge-based energies and an energy that encourages the boundary to pass through anchor points from the preprocessed images. For the LIB segmentation, the authors used local and global region-based energies, the anchor point-based energy, as well as a constraint promoting a boundary separation between the MAB and LIB. The data set consisted of 231 2D images (11 2D images per each of 21 subjects) extracted from 3D US images. The image slices were segmented five times each by a single observer using the algorithm and the manual method. Volume-based, region-based, and boundary distance-based metrics were used to evaluate accuracy. Moreover, repeated measures analysis was used to evaluate precision. RESULTS The algorithm yielded an absolute VWV difference of 5.0% +/- 4.3% with a segmentation bias of -0.9% +/- 6.6%. For the MAB and LIB segmentations, the method gave absolute volume differences of 2.5% +/- 1.8% and 5.6% +/- 3.0%, Dice coefficients of 95.4% +/- 1.6% and 93.1% +/- 3.1%, mean absolute distances of 0.2 +/- 0.1 and 0.2 +/- 0.1 mm, and maximum absolute distances of 0.6 +/- 0.3 and 0.7 +/- 0.6 mm, respectively. The coefficients of variation of the algorithm (5.1%) and manual methods (3.9%) were not significantly different, but the average time saved using the algorithm (2.8 min versus 8.3 min) was substantial. CONCLUSIONS The authors generated and tested a semiautomated carotid artery VWV measurement tool to provide measurements with reduced operator time and interaction, with high Dice coefficients, and with necessary required precision.

Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images

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.

3-D Carotid Multi-Region MRI Segmentation by Globally Optimal Evolution of Coupled Surfaces

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.

Fast interactive multi-region cardiac segmentation with linearly ordered labels

We present a novel and fast interactive approach to multi-modality cardiac image segmentation, which employs the linearly ordered surfaces as an additional constraint. We show using such a geometrical constraint helps to significantly reduce user interaction and improve the accuracy of segmentation results at the same time. We solve the proposed multiregion segmentation problem with the order constraints by means of convex optimization, resulting in a fast and reliable flow maximization approach which implicitly embeds the linear order prior without introducing extra computation load. In this regard, a new fully parallelized continuous max-flow algorithm is proposed and implemented using GPGPU to segment a 3D volume within one second. We demonstrate our results over pathological trans-esophageal echocardiogram, cardiac CT and delayed enhancement MRI data sets.

Rotational-Slice-Based prostate segmentation using level set with shape constraint for 3d end-firing TRUS guided biopsy

Prostate segmentation in 3D ultrasound images is an important step in the planning and treatment of 3D end-firing transrectal ultrasound (TRUS) guided prostate biopsy. A semi-automatic prostate segmentation method is presented in this paper, which integrates a modified distance regularization level set formulation with shape constraint to a rotational-slice-based 3D prostate segmentation method. Its performance, using different metrics, has been evaluated on a set of twenty 3D patient prostate images by comparison with expert delineations. The volume overlap ratio of 93.39 +/- 1.26% and the mean absolute surface distance of 1.16 +/- 0.34 mm were found in the quantitative validation result.

Dual optimization based prostate zonal segmentation in 3D MR images

Efficient and accurate segmentation of the prostate and two of its clinically meaningful sub-regions: the central gland (CG) and peripheral zone (PZ), from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, a novel multi-region segmentation approach is proposed to simultaneously segment the prostate and its two major sub-regions from only a single 3D T2-weighted (T2w) MR image, which makes use of the prior spatial region consistency and incorporates a customized prostate appearance model into the segmentation task. The formulated challenging combinatorial optimization problem is solved by means of convex relaxation, for which a novel spatially continuous max-flow model is introduced as the dual optimization formulation to the studied convex relaxed optimization problem with region consistency constraints. The proposed continuous max-flow model derives an efficient duality-based algorithm that enjoys numerical advantages and can be easily implemented on GPUs. The proposed approach was validated using 18 3D prostate T2w MR images with a body-coil and 25 images with an endo-rectal coil. Experimental results demonstrate that the proposed method is capable of efficiently and accurately extracting both the prostate zones: CG and PZ, and the whole prostate gland from the input 3D prostate MR images, with a mean Dice similarity coefficient (DSC) of 89.3±3.2% for the whole gland (WG), 82.2±3.0% for the CG, and 69.1±6.9% for the PZ in 3D body-coil MR images; 89.2±3.3% for the WG, 83.0±2.4% for the CG, and 70.0±6.5% for the PZ in 3D endo-rectal coil MR images. In addition, the experiments of intra- and inter-observer variability introduced by user initialization indicate a good reproducibility of the proposed approach in terms of volume difference (VD) and coefficient-of-variation (CV) of DSC.

Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology.

PURPOSE Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need. METHODS The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method. RESULTS The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs. CONCLUSIONS The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.

A fast convex optimization approach to segmenting 3d scar tissue from delayed-enhancement cardiac MR images

We propose a novel multi-region segmentation approach through a partially-ordered ports (POP) model to segment myocardial scar tissue solely from 3D cardiac delayed-enhancement MR images (DE-MRI). The algorithm makes use of prior knowledge of anatomical spatial consistency and employs customized label ordering to constrain the segmentation without prior knowledge of geometric representation. The proposed method eliminates the need for regional constraint segmentations, thus reduces processing time and potential sources of error. We solve the proposed optimization problem by means of convex relaxation and introduce its duality: the hierarchical continuous max-flow (HMF) model, which amounts to an efficient numerical solver to the resulting convex optimization problem. Experiments are performed over ten DE-MRI data sets. The results are compared to a FWHM (full-width at half-maximum) method and the inter- and intra-operator variabilities assessed.

Quantification and visualization of carotid segmentation accuracy and precision using a 2D standardized carotid map

This paper describes a framework for vascular image segmentation evaluation. Since the size of vessel wall and plaque burden is defined by the lumen and wall boundaries in vascular segmentation, these two boundaries should be considered as a pair in statistical evaluation of a segmentation algorithm. This work proposed statistical metrics to evaluate the difference of local vessel wall thickness (VWT) produced by manual and algorithm-based semi-automatic segmentation methods (ΔT) with the local segmentation standard deviation of the wall and lumen boundaries considered. ΔT was further approximately decomposed into the local wall and lumen boundary differences (ΔW and ΔL respectively) in order to provide information regarding which of the wall and lumen segmentation errors contribute more to the VWT difference. In this study, the lumen and wall boundaries in 3D carotid ultrasound images acquired for 21 subjects were each segmented five times manually and by a level-set segmentation algorithm. The (absolute) difference measures (i.e., ΔT, ΔW, ΔL and their absolute values) and the pooled local standard deviation of manually and algorithmically segmented wall and lumen boundaries were computed for each subject and represented in a 2D standardized map. The local accuracy and variability of the segmentation algorithm at each point can be quantified by the average of these metrics for the whole group of subjects and visualized on the 2D standardized map. Based on the results shown on the 2D standardized map, a variety of strategies, such as adding anchor points and adjusting weights of different forces in the algorithm, can be introduced to improve the accuracy and variability of the algorithm.