TY - CHAP A1 - Mukhopadhyay, Anirban T1 - Total Variation Random Forest: Fully automatic MRI segmentation in congenital heart disease T2 - RAMBO 2016, HVSMR 2016: Reconstruction, Segmentation, and Analysis of Medical Images Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-52280-7_17 VL - LNCS 10129 SP - 165 EP - 171 ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Morillo, Oscar A1 - Zachow, Stefan A1 - Lamecker, Hans T1 - Robust and Accurate Appearance Models Based on Joint Dictionary Learning Data from the Osteoarthritis Initiative T2 - Lecture Notes in Computer Science, Patch-Based Techniques in Medical Imaging. Patch-MI 2016 N2 - Deformable model-based approaches to 3D image segmentation have been shown to be highly successful. Such methodology requires an appearance model that drives the deformation of a geometric model to the image data. Appearance models are usually either created heuristically or through supervised learning. Heuristic methods have been shown to work effectively in many applications but are hard to transfer from one application (imaging modality/anatomical structure) to another. On the contrary, supervised learning approaches can learn patterns from a collection of annotated training data. In this work, we show that the supervised joint dictionary learning technique is capable of overcoming the traditional drawbacks of the heuristic approaches. Our evaluation based on two different applications (liver/CT and knee/MR) reveals that our approach generates appearance models, which can be used effectively and efficiently in a deformable model-based segmentation framework. Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-47118-1_4 VL - 9993 SP - 25 EP - 33 ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Porikli, Fatih A1 - Bhandarkar, Suchendra T1 - Detection and Characterization of Intrinsic Symmetry of 3D Shapes T2 - Proceedings of IEEE International Conference on Pattern Recognition N2 - A comprehensive framework for detection and characterization of partial intrinsic symmetry over 3D shapes is proposed. To identify prominent symmetric regions which overlap in space and vary in form, the proposed framework is decoupled into a Correspondence Space Voting (CSV) procedure followed by a Transformation Space Mapping (TSM) procedure. In the CSV procedure, significant symmetries are first detected by identifying surface point pairs on the input shape that exhibit local similarity in terms of their intrinsic geometry while simultaneously maintaining an intrinsic distance structure at a global level. To allow detection of potentially overlapping symmetric shape regions, a global intrinsic distance-based voting scheme is employed to ensure the inclusion of only those point pairs that exhibit significant intrinsic symmetry. In the TSM procedure, the Functional Map framework is employed to generate the final map of symmetries between point pairs. The TSM procedure ensures the retrieval of the underlying dense correspondence map throughout the 3D shape that follows a particular symmetry. The TSM procedure is also shown to result in the formulation of a metric symmetry space where each point in the space represents a specific symmetry transformation and the distance between points represents the complexity between the corresponding transformations. Experimental results show that the proposed framework can successfully analyze complex 3D shapes that possess rich symmetries. Y1 - 2016 ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Oksuz, Ilkay A1 - Bevilacqua, Marco A1 - Dharmakumar, Rohan A1 - Tsaftaris, Sotirios T1 - Data-Driven Feature Learning for Myocardial Segmentation of CP-BOLD MRI T2 - Functional Imaging and Modeling of the Heart N2 - 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. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-20309-6_22 VL - 9126 SP - 189 EP - 197 PB - Springer ER - TY - CHAP A1 - Mukhopadhyay, Anirban A1 - Oksuz, Ilkay A1 - Bevilacqua, Marco A1 - Dharmakumar, Rohan A1 - Tsaftaris, Sotirios T1 - Unsupervised myocardial segmentation for cardiac MRI T2 - Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 N2 - 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. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-24574-4_2 VL - LNCS 9351 SP - 12 EP - 20 ER - TY - CHAP A1 - Oksuz, Ilkay A1 - Mukhopadhyay, Anirban A1 - Bevilacqua, Marco A1 - Dharmakumar, Rohan A1 - Tsaftaris, Sotirios T1 - Dictionary Learning Based Image Descriptor for Myocardial Registration of CP-BOLD MR T2 - Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 N2 - 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 defini- tions 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 squared differences of these distinctive sparse representations are used to define a similarity term in the registration framework. The proposed descriptor is validated on a challenging dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canines. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-24571-3_25 VL - 9350 SP - 205 EP - 213 PB - Springer ER -