Department of Gastrointestinal Surgery, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8530, Japan.
Humanome Lab., Inc., 2-4-10-2F, Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Surg Today. 2022 Dec;52(12):1753-1758. doi: 10.1007/s00595-022-02508-5. Epub 2022 May 5.
We are attempting to develop a navigation system for safe and effective peripancreatic lymphadenectomy in gastric cancer surgery. As a preliminary study, we examined whether or not the peripancreatic dissection line could be learned by a machine learning model (MLM).
Among the 41 patients with gastric cancer who underwent radical gastrectomy between April 2019 and January 2020, we selected 6 in whom the pancreatic contour was relatively easy to trace. The pancreatic contour was annotated by a trainer surgeon in 1242 images captured from the video recordings. The MLM was trained using the annotated images from five of the six patients. The pancreatic contour was then segmented by the trained MLM using images from the remaining patient. The same procedure was repeated for all six combinations.
The median maximum intersection over union of each image was 0.708, which was higher than the threshold (0.5). However, the pancreatic contour was misidentified in parts where fatty tissue or thin vessels overlaid the pancreas in some cases.
The contour of the pancreas could be traced relatively well using the trained MLM. Further investigations and training of the system are needed to develop a practical navigation system.
我们正在尝试开发一种用于胃癌手术中安全有效胰周淋巴结清扫的导航系统。作为初步研究，我们检查了机器学习模型（MLM）是否可以学习胰周解剖线。
在 2019 年 4 月至 2020 年 1 月期间接受根治性胃切除术的 41 名胃癌患者中，我们选择了 6 名胰腺轮廓相对容易追踪的患者。培训外科医生在从视频记录中捕获的 1242 张图像中标注胰腺轮廓。使用来自其中 5 名患者的注释图像对 MLM 进行训练。然后，使用来自其余患者的图像，由经过训练的 MLM 对胰腺轮廓进行分割。对所有 6 种组合重复进行相同的步骤。
每张图像的最大交集中位数为 0.708，高于阈值（0.5）。然而，在某些情况下，胰腺上覆盖有脂肪组织或细血管的部位，胰腺轮廓被错误识别。
使用经过训练的 MLM 可以很好地追踪胰腺轮廓。需要进一步研究和系统培训，以开发实用的导航系统。
