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

Combining Registration Errors and Supervoxel Classification for Unsupervised Brain Anomaly Detection

Automatic detection of brain anomalies in MR images is challenging and complex due to intensity similarity between lesions and healthy tissues as well as the large variability in shape, size, and location among different anomalies. Even though discriminative models (supervised learning) are commonly used for this task, they require quite high-quality annotated training images, which are absent for most medical image analysis problems. Inspired by groupwise shape analysis, we adapt a recent fully unsupervised supervoxel-based approach (SAAD)—designed for abnormal asymmetry detection of the hemispheres—to detect brain anomalies from registration errors. Our method, called BADRESC, extracts supervoxels inside the right and left hemispheres, cerebellum, and brainstem, models registration errors for each supervoxel, and treats outliers as anomalies. Experimental results on MR-T1 brain images of stroke patients show that BADRESC outperforms a convolutional-autoencoder-based method and attains similar detection rates for hemispheric lesions in comparison to SAAD with substantially fewer false positives. It also presents promising detection scores for lesions in the cerebellum and brainstem.</p

An adaptive probabilistic atlas for anomalous brain segmentation in MR images

Automated segmentation of brain structures (objects) in MR three-dimensional (3D) images for quantitative analysis has been a challenge and probabilistic atlases (PAs) are among the most well-succeeded approaches. However, the existing models do not adapt to possible object anomalies due to the presence of a disease or a surgical procedure. Post-processing operation does not solve the problem, for example, tissue classification to detect and remove such anomalies inside the resulting segmentation mask, because segmentation errors on healthy tissues cannot be fixed. Such anomalies very often alter the shape and texture of the brain structures, making them different from the appearance of the model. In this paper, we present an effective and efficient adaptive probabilistic atlas, named AdaPro, to circumvent the problem and evaluate it on a challenging task - the segmentation of the left hemisphere, right hemisphere, and cerebellum, without pons and medulla, in 3D MR-T1 brain images of Epilepsy patients. This task is challenging due to temporal lobe resections, artifacts, and the absence of contrast in some parts between the structures of interest. Methods In AdaPro, we first build one probabilistic atlas per object of interest from a training set with normal 3D images and the corresponding 3D object masks. Second, we incorporate a texture classifier based on convex optimization which dynamically indicates the regions of the target 3D image where the PAs (shape constraints) should be further adapted. This strategy is mathematically more elegant and avoids problems with post-processing. Third, we add a new object-based delineation algorithm based on combinatorial optimization and diffusion filtering. AdaPro can then be used to locate and delineate the objects in the coordinate space of the atlas or of the test image. We also compare AdaPro with three other state-of-the-art methods: an statistical shape model based on synergistic object search and delineation, and two methods based on multi-atlas label fusion (MALF). We evaluate the methods quantitatively on 3D MR-T1 brain images of 2T and 3T from epilepsy patients, before and after temporal lobe resections, and on the template and native coordinate spaces. The results show that AdaPro is considerably faster and consistently more accurate than the baselines with statistical significance in both coordinate spaces. Conclusion AdaPro can be used as a fast and effective step for brain tissue segmentation and it can also be easily extended to segment subcortical brain structures. By choice of its components, probabilistic atlas, texture classifier, and delineation algorithm, it can also be extended to other organs and imaging modalities.461149404950CNPQ - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPESP – Fundação de Amparo à Pesquisa Do Estado De São Paulo303808/2018‐7; 308764/2015‐3; 403726/2016‐62013/07559‐3; 2014/12236‐1; 2018/08951‐

Combining Registration Errors and Supervoxel Classification for Unsupervised Brain Anomaly Detection

Automatic detection of brain anomalies in MR images is challenging and complex due to intensity similarity between lesions and healthy tissues as well as the large variability in shape, size, and location among different anomalies. Even though discriminative models (supervised learning) are commonly used for this task, they require quite high-quality annotated training images, which are absent for most medical image analysis problems. Inspired by groupwise shape analysis, we adapt a recent fully unsupervised supervoxel-based approach (SAAD)—designed for abnormal asymmetry detection of the hemispheres—to detect brain anomalies from registration errors. Our method, called BADRESC, extracts supervoxels inside the right and left hemispheres, cerebellum, and brainstem, models registration errors for each supervoxel, and treats outliers as anomalies. Experimental results on MR-T1 brain images of stroke patients show that BADRESC outperforms a convolutional-autoencoder-based method and attains similar detection rates for hemispheric lesions in comparison to SAAD with substantially fewer false positives. It also presents promising detection scores for lesions in the cerebellum and brainstem