Anirban Mukhopadhyay

Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes the left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to 1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and 2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website.11 http://www.cardiacatlas.org.

Data-driven feature learning for myocardial segmentation of CP-BOLD MRI

Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MR is capable of diagnosing an ongoing ischemia by detecting changes in myocardial intensity patterns at rest without any contrast and stress agents. Visualizing and detecting these changes require significant post-processing, including myocardial segmentation for isolating the myocardium. But, changes in myocardial intensity pattern and myocardial shape due to the heart’s motion challenge automated standard CINE MR myocardial segmentation techniques resulting in a significant drop of segmentation accuracy. We hypothesize that the main reason behind this phenomenon is the lack of discernible features. In this paper, a multi scale discriminative dictionary learning approach is proposed for supervised learning and sparse representation of the myocardium, to improve the myocardial feature selection. The technique is validated on a challenging dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canine subjects. The proposed method significantly outperforms standard cardiac segmentation techniques, including segmentation via registration, level sets and supervised methods for myocardial segmentation.

Morphological analysis of the left ventricular endocardial surface and its clinical implications

The complex morphological structure of the left ventricular endocardial surface and its relation to the severity of arterial stenosis has not yet been thoroughly investigated due to the limitations of conventional imaging techniques. By exploiting the recent developments in Multirow-Detector Computed Tomography (MDCT) scanner technology, the complex endocardial surface morphology of the left ventricle is studied and the cardiac segments affected by coronary arterial stenosis localized via analysis of Computed Tomography (CT) image data obtained from a 320-MDCT scanner. The non-rigid endocardial surface data is analyzed using an isometry-invariant Bag-of-Words (BOW) feature-based approach. The clinical significance of the analysis in identifying, localizing and quantifying the incidence and extent of coronary artery disease is investigated. Specifically, the association between the incidence and extent of coronary artery disease and the alterations in the endocardial surface morphology is studied. The results of the proposed approach on 15 normal data sets, and 12 abnormal data sets exhibiting coronary artery disease with varying levels of severity are presented. Based on the characterization of the endocardial surface morphology using the Bag-of-Words features, a neural network-based classifier is implemented to test the effectiveness of the proposed morphological analysis approach. Experiments performed on a strict leave-one-out basis are shown to exhibit a distinct pattern in terms of classification accuracy within the cardiac segments where the incidence of coronary arterial stenosis is localized.

Non-rigid shape correspondence and description using geodesic field estimate distribution

Non-rigid shape description and analysis is an unsolved problem in computer graphics. Shape analysis is a fast evolving research field due to the wide availability of 3D shape databases. Widely studied methods for this family of problems include the Gromov Hausdorff distance [1], Bag-of-Features [2] and diffusion geometry [3]. The limitations of the Euclidian distance measure in the context of isometric deformation have made geodesic distance a de-facto standard for describing a metric space for non-rigid shape analysis. In this work, we propose a novel geodesic field space-based approach to describe and analyze non-rigid shapes from a point correspondence perspective.

Shape analysis of the left ventricular endocardial surface and its application in detecting coronary artery disease

Coronary artery disease is the leading cause of morbidity and mortality worldwide. The complex morphological structure of the ventricular endocardial surface has not yet been studied properly due to the limitations of conventional imaging techniques. With the recent developments in Multi-Detector Computed Tomography (MDCT) scanner technology, we propose to study, in this paper, the complex endocardial surface morphology of the left ventricle via analysis of Computed Tomography (CT) image data obtained from a 320 Multi-Detector CT scanner. The CT image data is analyzed using a 3D shape analysis approach and the clinical significance of the analysis in detecting coronary artery disease is investigated. Global and local 3D shape descriptors are adapted for the purpose of shape analysis of the left ventricular endocardial surface. In order to study the association between the incidence of coronary artery disease and the alteration of the endocardial surface structure, we present the results of our shape analysis approach on 5 normal data sets, and 6 abnormal data sets with obstructive coronary artery disease. Based on the morphological characteristics of the endocardial surface as quantified by the shape descriptors, we implement a Linear Discrimination Analysis (LDA)-based classification algorithm to test the effectiveness of our shape analysis approach. Experiments performed on a strict leave-one-out basis are shown to achieve a classification accuracy of 81.8%.

Unsupervised Myocardial Segmentation for Cardiac MRI

Though unsupervised segmentation was a de-facto standard for cardiac MRI segmentation early on, recently cardiac MRI segmentation literature has favored fully supervised techniques such as Dictionary Learning and Atlas-based techniques. But, the benefits of unsupervised techniques e.g., no need for large amount of training data and better potential of handling variability in anatomy and image contrast, is more evident with emerging cardiac MR modalities. For example, CP-BOLD is a new MRI technique that has been shown to detect ischemia without any contrast at stress but also at rest conditions. Although CP-BOLD looks similar to standard CINE, changes in myocardial intensity patterns and shape across cardiac phases, due to the heart’s motion, BOLD effect and artifacts affect the underlying mechanisms of fully supervised segmentation techniques resulting in a significant drop in segmentation accuracy. In this paper, we present a fully unsupervised technique for segmenting myocardium from the background in both standard CINE MR and CP-BOLD MR. We combine appearance with motion information (obtained via Optical Flow) in a dictionary learning framework to sparsely represent important features in a low dimensional space and separate myocardium from background accordingly. Our fully automated method learns background-only models and one class classifier provides myocardial segmentation. The advantages of the proposed technique are demonstrated on a dataset containing CP-BOLD MR and standard CINE MR image sequences acquired in baseline and ischemic condition across 10 canine subjects, where our method outperforms state-of-the-art supervised segmentation techniques in CP-BOLD MR and performs at-par for standard CINE MR.

Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR

Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MRI is a new contrast agent- and stress-free imaging technique for the assessment of myocardial ischemia at rest. The precise registration among the cardiac phases in this cine type acquisition is essential for automating the analysis of images of this technique, since it can potentially lead to better specificity of ischemia detection. However, inconsistency in myocardial intensity patterns and the changes in myocardial shape due to the heart’s motion lead to low registration performance for state-of-the-art methods. This low accuracy can be explained by the lack of distinguishable features in CP-BOLD and inappropriate metric definitions in current intensity-based registration frameworks. In this paper, the sparse representations, which are defined by a discriminative dictionary learning approach for source and target images, are used to improve myocardial registration. This method combines appearance with Gabor and HOG features in a dictionary learning framework to sparsely represent features in a low dimensional space. The sum of absolute differences of these distinctive sparse representations are used to define a similarity term in the registration framework. The proposed approach is validated on a dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canines.

Addressing multi-label imbalance problem of surgical tool detection using CNN

PurposeA fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance.MethodsIn this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during convolutional neural network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance runtime prediction.ResultsQuantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection.ConclusionThe analysis on tool imbalance, backed by the empirical results, indicates the need and superiority of the proposed framework over state-of-the-art techniques.

Total Variation Random Forest: Fully Automatic MRI Segmentation in Congenital Heart Diseases

This paper proposes a fully automatic supervised segmentation technique for segmenting the great vessel and blood pool of pediatric cardiac MRIs of children with Congenital Heart Defects (CHD). CHD affects the overall anatomy of heart, rendering model-based segmentation framework infeasible, unless a large dataset of annotated images is available. However, the cardiac anatomy still retains distinct appearance patterns, which has been exploited in this work. In particular, Total Variation (TV) is introduced for solving the 3D disparity and noise removal problem. This results in homogeneous appearances within anatomical structures which is exploited further in a Random Forest framework. Context-aware appearance models are learnt using Random Forest (RF) for appearance-based prediction of great vessel and blood pool of an unseen subject during testing. We have obtained promising results on the HVSMR16 training dataset in a leave-one-out cross-validation.

Morphological Analysis of the Left Ventricular Endocardial Surface Using a Bag-of-Features Descriptor

The limitations of conventional imaging techniques have hitherto precluded a thorough and formal investigation of the complex morphology of the left ventricular (LV) endocardial surface and its relation to the severity of coronary artery disease (CAD). However, recent developments in high-resolution multirow-detector computed tomography (MDCT) scanner technology have enabled the imaging of the complex LV endocardial surface morphology in a single heartbeat. Analysis of high-resolution computed tomography images from a 320-MDCT scanner allows for the noninvasive study of the relationship between the percent diameter stenosis (DS) values of the major coronary arteries and localization of the cardiac segments affected by coronary arterial stenosis. In this paper, a novel approach for the analysis of the nonrigid LV endocardial surface from MDCT images, using a combination of rigid body transformation-invariant shape descriptors and a more generalized isometry-invariant Bag-of-Features descriptor, is proposed and implemented. The proposed approach is shown to be successful in identifying, localizing, and quantifying the incidence and extent of CAD and, thus, is seen to have a potentially significant clinical impact. Specifically, the association between the incidence and extent of CAD, determined via the percent DS measurements of the major coronary arteries, and the alterations in the endocardial surface morphology is formally quantified. The results of the proposed approach on 16 normal datasets and 16 abnormal datasets exhibiting CAD with varying levels of severity are presented. A multivariable regression test is employed to test the effectiveness of the proposed morphological analysis approach. Experiments performed on a strictly leave-one-out basis are shown to exhibit a distinct and interesting pattern in terms of the correlation coefficient values within the cardiac segments, where the incidence of coronary arterial stenosis is localized.