用改进自编码器先验提升血管分割精度，尤其擅长小结构识别。
Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
- 引入半过完备自编码器嵌入作为形状先验
- 在视网膜与肝脏血管数据集上分割效果优于U-Net
- 适合处理小血管等细节结构的医学图像分割任务
血血管提取在医学图像分析中受到广泛关注。自动血管分割对计算机辅助诊断、治疗或手术规划具有重要意义。尽管基于U-Net的架构在提取大尺度解剖结构方面表现良好，但在自动勾画血管系统方面仍存在挑战，尤其是在现有数据集稀缺的情况下。本文提出一种新方法，将半过完备卷积自编码器（S-OCAE）的嵌入作为深度分割中的形状先验。相较于标准卷积自编码器（CAE），S-OCAE通过一个过完备分支将数据投影到更高维空间，以更好地刻画微小结构。在公开的DRIVE和3D-IRCADb数据集上进行的视网膜与肝脏血管提取实验表明，该方法在性能上优于未使用形状先验的U-Net以及使用传统CAE先验的U-Net。
原文摘要 · Abstract (English)
The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy or surgical planning. Despite a good ability to extract large anatomical structures, the capacity of U-Net inspired architectures to automatically delineate vascular systems remains a major issue, especially given the scarcity of existing datasets. In this paper, we present a novel approach that integrates into deep segmentation shape priors from a Semi-Overcomplete Convolutional Auto-Encoder (S-OCAE) embedding. Compared to standard Convolutional Auto-Encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize tiny structures. Experiments on retinal and liver vessel extraction, respectively performed on publicly-available DRIVE and 3D-IRCADb datasets, highlight the effectiveness of our method compared to U-Net trained without and with shape priors from a traditional CAE.
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