融合空间与深度特征，精准分割医学图像中的细微结构
Rich-U-Net: A medical image segmentation model for fusing spatial depth features and capturing minute structural details
- 通过多层级特征融合，同时捕捉空间位置与深度信息
- 在ISIC2018等四个数据集上，Dice、IoU、HD95均优于现有模型
- 适合需要高精度细节分割的医学影像分析任务
医学图像分割对疾病分析具有重要意义。深度神经网络可帮助医生从复杂医学图像中提取感兴趣区域，提升诊断准确率并支持治疗方案制定。然而，当前多数方法在精确提取空间信息及挖掘复杂结构方面表现不足。本文提出Rich-U-Net模型，有效融合空间与深度特征，增强对复杂医学图像中细小结构和复杂细节的检测能力。通过多层次、多维度的特征融合与优化策略，该模型实现了精细结构定位与高精度分割。在ISIC2018、BUSI、GLAS和CVC数据集上的实验表明，Rich-U-Net在Dice、IoU和HD95指标上均超越现有先进模型。
原文摘要 · Abstract (English)
Medical image segmentation is of great significance in analysis of illness. The use of deep neural networks in medical image segmentation can help doctors extract regions of interest from complex medical images, thereby improving diagnostic accuracy and enabling better assessment of the condition to formulate treatment plans. However, most current medical image segmentation methods underperform in accurately extracting spatial information from medical images and mining potential complex structures and variations. In this article, we introduce the Rich-U-Net model, which effectively integrates both spatial and depth features. This fusion enhances the model's capability to detect fine structures and intricate details within complex medical images. Our multi-level and multi-dimensional feature fusion and optimization strategies enable our model to achieve fine structure localization and accurate segmentation results in medical image segmentation. Experiments on the ISIC2018, BUSI, GLAS, and CVC datasets show that Rich-U-Net surpasses other state-of-the-art models in Dice, IoU, and HD95 metrics.
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