Unsupervised brain anomaly detection in MR images

Brain disorders are characterized by morphological deformations in shape and size of (sub)cortical structures in one or both hemispheres. These deformations cause deviations from the normal pattern of brain asymmetries, resulting in asymmetric lesions that directly affect the patient’s condition. Unsupervised methods aim to learn a model from unlabeled healthy images, so that an unseen image that breaks priors of this model, i.e., an outlier, is considered an anomaly. Consequently, they are generic in detecting any lesions, e.g., coming from multiple diseases, as long as these notably differ from healthy training images. This thesis addresses the development of solutions to leverage unsupervised machine learning for the detection/analysis of abnormal brain asymmetries related to anomalies in magnetic resonance (MR) images. First, we propose an automatic probabilistic-atlas-based approach for anomalous brain image segmentation. Second, we explore an automatic method for the detection of abnormal hippocampi from abnormal asymmetries based on deep generative networks and a one-class classifier. Third, we present a more generic framework to detect abnormal asymmetries in the entire brain hemispheres. Our approach extracts pairs of symmetric regions — called supervoxels — in both hemispheres of a test image under study. One-class classifiers then analyze the asymmetries present in each pair. Experimental results on 3D MR-T1 images from healthy subjects and patients with a variety of lesions show the effectiveness and robustness of the proposed unsupervised approaches for brain anomaly detection

Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images

Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing a reliable diagnostic value. Similarly, quantification of the myocardial mechanics throughout the cardiac cycle, analysis of the activation patterns in the heart via electrocardiography (ECG) signals, serve as good cardiac diagnosis indicators. Furthermore, high quality anatomical models of the heart can be used in planning and guidance of minimally invasive interventions under the assistance of image guidance. The most crucial step for the above mentioned applications is to segment the ventricles and myocardium from the acquired cardiac image data. Although the manual delineation of the heart structures is deemed as the gold-standard approach, it requires significant time and effort, and is highly susceptible to inter- and intra-observer variability. These limitations suggest a need for fast, robust, and accurate semi- or fully-automatic segmentation algorithms. However, the complex motion and anatomy of the heart, indistinct borders due to blood flow, the presence of trabeculations, intensity inhomogeneity, and various other imaging artifacts, makes the segmentation task challenging. In this work, we present and evaluate segmentation algorithms for multi-modal, multi-dimensional cardiac image datasets. Firstly, we segment the left ventricle (LV) blood-pool from a tri-plane 2D+time trans-esophageal (TEE) ultrasound acquisition using local phase based filtering and graph-cut technique, propagate the segmentation throughout the cardiac cycle using non-rigid registration-based motion extraction, and reconstruct the 3D LV geometry. Secondly, we segment the LV blood-pool and myocardium from an open-source 4D cardiac cine Magnetic Resonance Imaging (MRI) dataset by incorporating average atlas based shape constraint into the graph-cut framework and iterative segmentation refinement. The developed fast and robust framework is further extended to perform right ventricle (RV) blood-pool segmentation from a different open-source 4D cardiac cine MRI dataset. Next, we employ convolutional neural network based multi-task learning framework to segment the myocardium and regress its area, simultaneously, and show that segmentation based computation of the myocardial area is significantly better than that regressed directly from the network, while also being more interpretable. Finally, we impose a weak shape constraint via multi-task learning framework in a fully convolutional network and show improved segmentation performance for LV, RV and myocardium across healthy and pathological cases, as well as, in the challenging apical and basal slices in two open-source 4D cardiac cine MRI datasets. We demonstrate the accuracy and robustness of the proposed segmentation methods by comparing the obtained results against the provided gold-standard manual segmentations, as well as with other competing segmentation methods