Noort F van den, Borg F Ter, Guitink A, Faber J, Wolterink J M
Department of Applied Mathematics, Technical Medical Center, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, the Netherlands.
Department of Gastroenterology & Hepatology, Deventer Hospital, Deventer, the Netherlands.
Tech Coloproctol. 2024 Dec 13;29(1):20. doi: 10.1007/s10151-024-03056-5.
Bowel-preserving local resection of early rectal cancer is less successful if the tumor infiltrates the muscularis propria as opposed to submucosal infiltration only. Magnetic resonance imaging currently lacks the spatial resolution to provide a reliable estimation of the infiltration depth. Endoscopic ultrasound (EUS) has better resolution, but its interpretation is investigator dependent. We hypothesize that automated image segmentation of EUS could be a way to standardize EUS interpretation.
EUS media and outcome data were collected prospectively. Based on 373 expert manual segmentations, a convolutional neural network was developed to perform segmentation of the submucosa, muscularis propria, and tumors. The mean surface distance (MSD), maximal distance between segmentations (Hausdorff distance; HDD), and overlap (Dice similarity index; DSI) were calculated.
The median MSD and HDD values were 3.2 and 17.7 pixels for the tumor, 3.4 and 24.7 pixels for the submucosa, and 2.6 and 20.0 pixels for the muscularis propria, respectively. The median DSI values for the tumor, submucosa, and muscularis propria were 0.82, 0.57, and 0.59, respectively. These values reflect good agreement between manual and deep learning segmentation.
This study found encouraging results of using automated analysis of EUS images of early rectal cancer, supporting further exploration in clinical practice.
与仅浸润黏膜下层相比，早期直肠癌保肠局部切除在肿瘤浸润固有肌层时成功率较低。目前磁共振成像缺乏空间分辨率，无法可靠估计浸润深度。内镜超声（EUS）具有更好的分辨率，但其解读依赖于检查者。我们假设EUS的自动图像分割可能是使EUS解读标准化的一种方法。
前瞻性收集EUS介质和结果数据。基于373次专家手动分割，开发了一个卷积神经网络来对黏膜下层、固有肌层和肿瘤进行分割。计算平均表面距离（MSD）、分割之间的最大距离（豪斯多夫距离；HDD）和重叠度（骰子相似性指数；DSI）。
肿瘤的MSD和HDD中位数分别为3.2像素和17.7像素，黏膜下层为3.4像素和24.7像素，固有肌层为2.6像素和20.0像素。肿瘤、黏膜下层和固有肌层的DSI中位数分别为0.82、0.57和0.59。这些值反映了手动分割和深度学习分割之间的良好一致性。
本研究发现对早期直肠癌EUS图像进行自动分析的结果令人鼓舞，支持在临床实践中进一步探索。
