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.

翻译：医学图像诊断领域随着计算机辅助诊断和手术系统的整合取得了显著进展。然而，尤其是在实现精确图像分割方面仍存在挑战。尽管深度学习技术展现出潜力，但资源有限、收敛缓慢以及类别不平衡等障碍限制了其有效性。传统基于分块的方法虽然常见，但难以捕捉复杂的肿瘤边界，且常导致样本冗余，从而降低了计算效率和特征质量。为解决这些问题，本研究提出了一种以肿瘤本身为中心的创新分块图像分析方法。这种新颖的肿瘤中心分块方法旨在解决类别不平衡和边界缺陷问题，从而实现聚焦且精确的肿瘤分割。通过将分块与肿瘤的解剖结构对齐，该技术增强了特征提取的准确性并降低了计算负担。实验结果表明类别不平衡得到改善，在使用轻量级简单U-Net时，整体肿瘤、核心肿瘤和增强肿瘤的分割得分分别为0.78、0.76和0.71。该方法显示出增强医学图像分割和改善计算机辅助诊断系统的潜力。