TY - JOUR A1 - von Tycowicz, Christoph A1 - Ambellan, Felix A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - An Efficient Riemannian Statistical Shape Model using Differential Coordinates JF - Medical Image Analysis N2 - We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidean structure. A key advantage of our framework is that statistics in a manifold shape space becomes numerically tractable improving performance by several orders of magnitude over state-of-the-art. We show that our Riemannian model is well suited for the identification of intra-population variability as well as inter-population differences. In particular, we demonstrate the superiority of the proposed model in experiments on specificity and generalization ability. We further derive a statistical shape descriptor that outperforms the standard Euclidean approach in terms of shape-based classification of morphological disorders. Y1 - 2018 U6 - https://doi.org/10.1016/j.media.2017.09.004 VL - 43 IS - 1 SP - 1 EP - 9 ER - 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 - JOUR A1 - Mukhopadhyay, Anirban A1 - Bhandarkar, Suchendra T1 - Biharmonic Density Estimate - a scale space descriptor for 3D deformable surfaces JF - Pattern Analysis and Application Y1 - 2017 U6 - https://doi.org/10.1007/s10044-017-0610-2 SP - 1 EP - 13 ER - TY - JOUR A1 - Sahu, Manish A1 - Mukhopadhyay, Anirban A1 - Szengel, Angelika A1 - Zachow, Stefan T1 - Addressing multi-label imbalance problem of Surgical Tool Detection using CNN JF - International Journal of Computer Assisted Radiology and Surgery N2 - Purpose: A 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. Methods: In 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 run time prediction. Results: Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection. Conclusion: The analysis on tool imbalance, backed by the empirical results indicates the need and superiority of the proposed framework over state-of-the-art techniques. Y1 - 2017 UR - https://link.springer.com/article/10.1007/s11548-017-1565-x U6 - https://doi.org/10.1007/s11548-017-1565-x N1 - Selected for final oral presentation VL - 12 IS - 6 SP - 1013 EP - 1020 PB - Springer ER - TY - JOUR A1 - Suinesiaputra, Avan A1 - Albin, Pierre A1 - Alba, Xenia A1 - Alessandrini, Martino A1 - Allen, Jack A1 - Bai, Wenjia A1 - Cimen, Serkan A1 - Claes, Peter A1 - Cowan, Brett A1 - D'hooge, Jan A1 - Duchateau, Nicolas A1 - Ehrhardt, Jan A1 - Frangi, Alejandro A1 - Gooya, Ali A1 - Grau, Vicente A1 - Lekadir, Karim A1 - Lu, Allen A1 - Mukhopadhyay, Anirban A1 - Oksuz, Ilkay A1 - Parajuli, Nripesh A1 - Pennec, Xavier A1 - Pereanez, Marco A1 - Pinto, Catarina A1 - Piras, Paolo A1 - Rohe, Marc-Michael A1 - Rueckert, Daniel A1 - Saring, Dennis A1 - Sermesant, Maxime A1 - Siddiqi, Kaleem A1 - Tabassian, Mahdi A1 - Teresi, Lusiano A1 - Tsaftaris, Sotirios A1 - Wilms, Matthias A1 - Young, Alistair A1 - Zhang, Xingyu A1 - Medrano-Gracia, Pau T1 - Statistical shape modeling of the left ventricle: myocardial infarct classification challenge JF - IEEE Journal of Biomedical and Health Informatics N2 - 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 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 website1. Y1 - 2017 U6 - https://doi.org/10.1109/JBHI.2017.2652449 IS - 99 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 -