摘要：

从病理全切片图像中自动准确分割肾动脉血管是肾脏疾病诊断的前提, 在肾脏疾病的诊断中起着重要的作用。现有的方法大多集中于检测和分割突出的肾小球, 很少有文献关注肾动脉血管的分割, 因为其形态外观高度可变, 边界不明确。针对以上问题, 提出了一种级联检测和分割框架, 用于精确分割和定量分析肾动脉血管。第一阶段构建多窗口自适应校准的肾动脉检测网络(RADNet), 用于定位肾动脉区域; 第二阶段设计分割网络, 将高效的通道空间注意和视觉Transformer结合到卷积网络(U-Net)中, 以准确分割肾动脉壁和管腔。最后, 通过计算定量结果与临床信息的相关性, 进行定量分析。检测网络采用多尺度自适应标定方法, 能够对动脉区域进行定位; 分割网络利用Transformer以及高效通道和空间注意机制, 能够更好地提取形态外观复杂、边界不明确的动脉壁和管腔。实验结果表明, 与之前的模型相比, 所提出的框架在小动脉血管检测和分割方面的性能取得了显著的提高。此外, 该方法在医学图像中小病变的分割和量化方面具有很大的潜力和临床应用价值。

关键词: 肾动脉, 检测与分割, 直径测量, 量化分析, 深度学习

Abstract:

Automatic and accurate segmentation of renal arterial vessels from pathologic whole section images is crucial and a prerequisite for the diagnosis of renal diseases. Most existing methods focus on detecting and segmenting prominent glomeruli. Studies focusing on segmenting arterial vessels are scarce because of the highly variable morphological appearance and unclear boundaries of such vessels. To address these issues, a cascade detection and segmentation framework is proposed for accurate segmentation and quantitative analysis of renal arterial vessels. In the first stage, a multi-window adaptively calibrated Renal Artery Detection Network (RADNet) is constructed to locate the renal artery region. In the second stage, a segmentation network is designed to accurately segment the renal artery walls and lumens by combining efficient channel spatial attention and a visual Transformer into a convolutional network (U-Net). Finally, a quantitative analysis is performed by calculating the correlation between the quantitative results and clinical information. The detection network adopts a multiscale adaptive calibration method, which can localize the arterial region; the segmentation network utilizes the Transformer and efficient channel and spatial attention mechanisms to better extract arterial walls and lumens with complex morphological appearances and ill-defined boundaries. The experimental results show that the proposed framework achieves significant improvements in the detection and segmentation of small arterial vessels compared to previous models. In addition, this method has great potential for clinical applications in the segmentation and quantification of small lesions in medical images.

Key words: renal artery, detection and segmentation, diameter measurement, quantification analysis, deep learning