Development of Deep Learning based Brain Stroke Abnormality Segmentation Methods in Neuroimaging Modalities

Abstract

Accurate segmentation of stroke lesions in neuroimaging data is a critical step for clinical diagnosis, treatment planning, and monitoring patient recovery. However, it is challenging due to several factors, including high variability of lesion size and appearance, limited annotated data, and the complexity of integrating information from multiple imaging modalities such as CT perfusion (CTP) and multimodal MRI. This thesis presents deep learning-based models that address these challenges through a hybrid fusion strategy for effective fusion of multimodalities, multi-scale processing for handling the scale variations in the lesions, and intra-domain transfer learning for handling the data limitation issue. The work presents the deep learning-based lesion segmentation on two different imaging modalities, such as CTP and MRI. The first part of the work focuses on exploring multiple fusion strategies, such as early, late, bottleneck, and hierarchical fusion, to identify the optimal way of integrating the multimodal CTP data to improve lesion localization and boundary refinement. While each fusion method has different advantages, they also exhibit some limitations when used independently. To overcome this, we proposed a hybrid fusion that combines early and bottleneck fusion. Furthermore, a simple fusion of features from sources may not yield optimal results. Therefore, attention modules are integrated into the fusion process to dynamically emphasize informative features across modalities through non-local block concepts. This enhanced attention-based hybrid fusion is found to be effective in enhancing lesion location and boundary delineation in CTP data while generating faster inferences. To address the lesion size variability, the second part of the thesis introduces a twolevel multiscale processing framework.