用于增强医学图像分割的以肿瘤为中心的分块方法
Tumor-Centered Patching for Enhanced Medical Image Segmentation
AI总结：
针对医学图像分割中类别不平衡、边界捕捉难、计算效率低等问题，提出以肿瘤为中心的分块方法，通过分块对齐肿瘤解剖背景提升特征提取精度，用轻量simple U-Net取得了更优的肿瘤分割效果，可助力完善计算机辅助诊断系统。
摘要：
随着计算机辅助诊断与手术系统的融合，医学图像诊断领域已取得显著进展。但目前仍存在挑战，尤其是在实现精确图像分割方面。尽管深度学习技术展现出潜力，但资源有限、收敛缓慢和类别不平衡等障碍阻碍了其有效性。传统的基于分块的方法虽然应用广泛，却难以捕捉复杂的肿瘤边界，且往往产生冗余样本，损害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核心的创新型分块图像分析方法。这种全新的以肿瘤为中心的分块方法旨在解决类别不平衡和边界表征不足的问题，实现聚焦且准确的肿瘤分割。通过让分块与肿瘤的解剖学背景对齐，该技术提升了特征提取的准确性并降低了计算负荷。实验结果表明，该方法改善了类别不平衡问题，使用轻量级的simple U-Net时，全肿瘤、肿瘤核心和增强肿瘤的分割得分分别为0.78、0.76和0.71。该方法有望增强医学图像分割效果，完善计算机辅助诊断系统。
英文摘要：
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.
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