  The realm of medical image diagnosis has advanced significantly with the
integration of computer-aided diagnosis and surgical systems. However,
challenges persist, particularly in achieving precise image segmentation. While
deep learning techniques show potential, obstacles like limited resources, slow
convergence, and class imbalance impede their effectiveness. Traditional
patch-based methods, though common, struggle to capture intricate tumor
boundaries and often lead to redundant samples, compromising computational
efficiency and feature quality. To tackle these issues, this research
introduces an innovative approach centered on the tumor itself for patch-based
image analysis. This novel tumor-centered patching method aims to address the
class imbalance and boundary deficiencies, enabling focused and accurate tumor
segmentation. By aligning patches with the tumor's anatomical context, this
technique enhances feature extraction accuracy and reduces computational load.
Experimental results demonstrate improved class imbalance, with segmentation
scores of 0.78, 0.76, and 0.71 for whole, core, and enhancing tumors,
respectively using a lightweight simple U-Net. This approach shows potential
for enhancing medical image segmentation and improving computer-aided diagnosis
systems.
