Improving Deep Learning for Brain Localization

When utilizing advanced medical imaging techniques like high-resolution magnetic resonance imaging (MRI), the measurement of critical parameters such as total cranial volume (TCV) and the volume of the posterior cranial fossa (PCF) plays a pivotal role in volumetric brain analysis. Whole-brain segmentation, a non-invasive methodology, facilitates the precise delineation of various brain regions. Moreover, to ensure the protection of individuals' privacy, a growing trend involves sharing neuroimaging data in a format where the skull information has been removed. Hence, the development of a robust deep learning algorithm that can perform accurate brain segmentation, whether with or without prior skull removal, is an intriguing challenge. One major obstacle in this endeavor is the limited availability of manually annotated reference datasets containing complete whole-brain information and TCV/PCF measurements. In this research, we employ U-Net-based tiling techniques to perform comprehensive brain segmentation on MRI scans, both with and without the skull. Simultaneously, we aim to estimate cranial volumes. To address the shortage of fully annotated whole-brain volumes, we propose a transfer learning approach. Initially, we pre-train the U-Net model using a substantial dataset comprising BrainCOLOR atlases that have been segmented using multiple references, including TCV and PCF measurements. Subsequently, we fine-tune these pre-trained models using a set of carefully curated BrainCOLOR atlases containing precise TCV and PCF labels. This process enables us to improve the accuracy of our brain segmentation models. Furthermore, we extend our methodology to accommodate MRI scans that have undergone skull removal, ensuring the versatility and applicability of our approach across various imaging scenarios.