Abstract

 

Brain segmentation of stroke patients can facilitate brain modeling for electrical non-invasive brain stimulation, a therapy for stimulating brain function using an electric current. However, it remains challenging owing to its time-consuming, labor-dependent, and complicated pipeline. In addition, conventional tools that define lesions into one region rather than distinguishing between the stroke-affected regions and cerebrospinal fluid can lead to inaccurate treatment results. In this study, we first define a novel stroke-affected region as a detailed sub-region of the conventionally defined lesion. Subsequently, a novel comprehensive framework is proposed to segment head-brain and fine-level stroke-affected regions for normal controls and chronic stroke patients. The proposed framework consists of a time-efficient and precise deep learning-based segmentation model. The experiment results indicate that the proposed method perform better than the conventional deep learning-based segmentation model in terms of the evaluation metrics. The proposed method would be a valuable addition to brain modeling for non-invasive neuromodulation.