<div>Brain tumor sub region segmentation is a challenging problem in Magnetic Resonance imaging. The tumor regions tend to suffer from lack of homogeneity, textural differences, variable location, and their ability to proliferate into surrounding tissue. </div><div> The segmentation task thus requires an algorithm which can be indifferent to such influences and robust to external interference. In this work we propose a conditional generative adversarial network which learns off multiple planes of reference. Using this learning, we evaluate the quality of the segmentation and back propagate the loss for improving the learning. The results produced by the network show competitive quality in both the training and the testing data-set.</div><div><br></div>

This thesis investigates the potential of federated learning (FL) in medical image analysis, addressing the challenges posed by data privacy regulations in accessing medical datasets. The motivation stems from the increasing interest in artificial intelligence (AI)research, particularly in medical imaging for tumor detection using magnetic resonance imaging (MRI) and computer tomography (CT) scans. However, data accessibility remains a significant hurdle due to privacy regulations like the General Data Protection Regulation (GDPR). FL emerges as a solution by focusing on sharing network parameters instead of raw medical data, thus ensuring patient confidentiality. The aims of the study are to understand the requirements for FL models to perform comparably to centrally trained models, explore the impact of different aggregation functions, assess dataset heterogeneity, and evaluate the generalization of FL models. To achieve these goals, this thesis uses the BraTS 2021 dataset, which contains 1251 cases of brain tumor volumes from 23 distinct sites, with different distributions of the data across 3-8 nodes in a federation. The federation is set up to perform brain tumor segmentation, using different forms of aggregationfunctions (FedAvg. FedOpt, and FedProx) to finalize a global model. The final FL models demonstrate similar performance to that of centralized and local models, with minor variations. However, FL models’ performance varies depending on the dataset distribution and aggregation method used. Additionally, this study explores the impact of privacy-preserving techniques, such as differential privacy (DP), on FL model performance. While DP methods generally result in lower performance compared to non-DP methods, their effectiveness varies across different data distributions, and aggregation functions.

Abstract : Malignant brain tumors are the second leading cause of cancer related deaths in children under 20. There are nearly 700,000 people in the U.S. living with a brain tumor and 17,000 people are likely to loose their lives due to primary malignant and central nervous system brain tumor every year. To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI scan of the brain is acquired followed by a manual examination of the scan by an expert who looks for lesions (i.e. cluster of cells which deviate from healthy tissue). For treatment purposes, the tumor and its sub-regions are outlined in a procedure known as brain tumor segmentation . Although brain tumor segmentation is primarily done manually, it is very time consuming and the segmentation is subject to variations both between observers and within the same observer. To address these issues, a number of automatic and semi-automatic methods have been proposed over the years to help physicians in the decision making process. Methods based on machine learning have been subjects of great interest in brain tumor segmentation. With the advent of deep learning methods and their success in many computer vision applications such as image classification, these methods have also started to gain popularity in medical image analysis. In this thesis, we explore different machine learning and deep learning methods applied to brain tumor segmentation. / Résumé: Les tumeurs malignes au cerveau sont la deuxième cause principale de décès chez les enfants de moins de 20 ans. Il y a près de 700 000 personnes aux États-Unis vivant avec une tumeur au cerveau, et 17 000 personnes sont chaque année à risque de perdre leur vie suite à une tumeur maligne primaire dans le système nerveu central. Pour identifier de façon non-invasive si un patient est atteint d'une tumeur au cerveau, une image IRM du cerveau est acquise et analysée à la main par un expert pour trouver des lésions (c.-à-d. un groupement de cellules qui diffère du tissu sain). Une tumeur et ses régions doivent être détectées à l'aide d'une segmentation pour aider son traitement. La segmentation de tumeur cérébrale et principalement faite à la main, c'est une procédure qui demande beaucoup de temps et les variations intra et inter expert pour un même cas varient beaucoup. Pour répondre à ces problèmes, il existe beaucoup de méthodes automatique et semi-automatique qui ont été proposés ces dernières années pour aider les praticiens à prendre des décisions. Les méthodes basées sur l'apprentissage automatique ont suscité un fort intérêt dans le domaine de la segmentation des tumeurs cérébrales. L'avènement des méthodes de Deep Learning et leurs succès dans maintes applications tels que la classification d'images a contribué à mettre de l'avant le Deep Learning dans l'analyse d'images médicales. Dans cette thèse, nous explorons diverses méthodes d'apprentissage automatique et de Deep Learning appliquées à la segmentation des tumeurs cérébrales.

A brain tumor is an abnormal growth of cells within the brain, which can be categorized into primary and secondary tumor types. The most common type of primary tumors in adults are gliomas, which can be further classified into high-grade gliomas (HGGs) and low-grade gliomas (LGGs). Approximately 50% of patients diagnosed with HGG pass away within 1-2 years. Therefore, the early detection and prompt treatment of brain tumors are essential for effective management and improved patient outcomes.  Brain tumor segmentation is a task in medical image analysis that entails distinguishing brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. Computer vision algorithms and deep learning models capable of analyzing medical images can be leveraged for brain tumor segmentation. These algorithms and models have the potential to provide automated, reliable, and non-invasive screening for brain tumors, thereby enabling earlier and more effective treatment. For a considerable time, Convolutional Neural Networks (CNNs), including the U-Net, have served as the standard backbone architectures employed to address challenges in computer vision. In recent years, the Transformer architecture, which already has firmly established itself as the new state-of-the-art in the field of natural language processing (NLP), has been adapted to computer vision tasks. The Vision Transformer (ViT) and the Swin Transformer are two architectures derived from the original Transformer architecture that have been successfully employed for image analysis. The emergence of Transformer based architectures in the field of computer vision calls for an investigation whether CNNs can be rivaled as the de facto architecture in this field.  This thesis compares the performance of four model architectures, namely the Swin Transformer, the Vision Transformer, the 2D U-Net, and the 2D U-Net which is implemented with the nnU-Net framework. These model architectures are trained using increasing amounts of brain tumor images from the BraTS 2020 dataset and subsequently evaluated on the task of brain tumor segmentation for both HGG and LGG together, as well as HGG and LGG individually. The model architectures are compared on total training time, segmentation time, GPU memory usage, and on the evaluation metrics Dice Coefficient, Jaccard Index, precision, and recall. The 2D U-Net implemented using the nnU-Net framework performs the best in correctly segmenting HGG and LGG, followed by the Swin Transformer, 2D U-Net, and Vision Transformer. The Transformer based architectures improve the least when going from 50% to 100% of training data. Furthermore, when data augmentation is applied during training, the nnU-Net outperforms the other model architectures, followed by the Swin Transformer, 2D U-Net, and Vision Transformer. The nnU-net benefited the least from employing data augmentation during training, while the Transformer based architectures benefited the most.  In this thesis we were able to perform a successful comparative analysis effectively showcasing the distinct advantages of the four model architectures under discussion. Future comparisons could incorporate training the model architectures on a larger set of brain tumor images, such as the BraTS 2021 dataset. Additionally, it would be interesting to explore how Vision Transformers and Swin Transformers, pre-trained on either ImageNet- 21K or RadImageNet, compare to the model architectures of this thesis on brain tumor segmentation.

<p>Automatic brain tumor segmentation in Magnetic Resonance Imaging scans is essential for the diagnosis, treatment, and surgery of cancerous tumors. However, identifying the hardly detectable tumors poses a considerable challenge, which are usually of different sizes, irregular shapes, and vague invasion areas. Current advancements have not yet fully leveraged the dynamics in the multiple modalities of MRI, since they usually treat multi-modality as multi-channel, and the early channel merging may not fully reveal inter-modal couplings and complementary patterns. In this thesis, we propose a novel deep cross-attention learning algorithm that maximizes the subtle dynamics mining from each of the input modalities and then boosts feature fusion capability. More specifically, we have designed a Multimodal Cross-Attention Module (MM-CAM), equipped with a 3D Multimodal Feature Rectification and Feature Fusion Module. Extensive experiments have shown that the proposed novel deep learning architecture, empowered by the innovative MM- CAM, produces higher-quality segmentation masks of the tumor subregions. Further, we have enhanced the algorithm with image matting refinement techniques. We propose to integrate a Progressive Refinement Module (PRM) and perform Cross-Subregion Refinement (CSR) for the precise identification of tumor boundaries. A Multiscale Dice Loss was also successfully employed to enforce additional supervision for the auxiliary segmentation outputs. This enhancement will facilitate effectively matting-based refinement for medical image segmentation applications. Overall, this thesis, with deep learning, transformer-empowered pattern mining, and sophisticated architecture designs, will greatly advance deep brain dynamics and images mining for tumor detection and precision medicine.</p>