Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
Information Strategy Office, Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
Int J Comput Assist Radiol Surg. 2023 May;18(5):795-805. doi: 10.1007/s11548-022-02821-x. Epub 2023 Mar 13.
Endoscopic submucosal dissection (ESD) is a minimally invasive treatment for early gastric cancer. However, perforations may happen and cause peritonitis during ESD. Thus, there is a potential demand for a computer-aided diagnosis system to support physicians in ESD. This paper presents a method to detect and localize perforations from colonoscopy videos to avoid perforation ignoring or enlarging by ESD physicians.
We proposed a training method for YOLOv3 by using GIoU and Gaussian affinity losses for perforation detection and localization in colonoscopic images. In this method, the object functional contains the generalized intersection over Union loss and Gaussian affinity loss. We propose a training method for the architecture of YOLOv3 with the presented loss functional to detect and localize perforations precisely.
To qualitatively and quantitatively evaluate the presented method, we created a dataset from 49 ESD videos. The results of the presented method on our dataset revealed a state-of-the-art performance of perforation detection and localization, which achieved 0.881 accuracy, 0.869 AUC, and 0.879 mean average precision. Furthermore, the presented method is able to detect a newly appeared perforation in 0.1 s.
The experimental results demonstrated that YOLOv3 trained by the presented loss functional were very effective in perforation detection and localization. The presented method can quickly and precisely remind physicians of perforation happening in ESD. We believe a future CAD system can be constructed for clinical applications with the proposed method.
内镜黏膜下剥离术（ESD）是一种治疗早期胃癌的微创方法。然而，在 ESD 过程中可能会发生穿孔，并导致腹膜炎。因此，需要开发一种计算机辅助诊断系统，以支持 ESD 医生的工作。本文提出了一种从结肠镜视频中检测和定位穿孔的方法，以避免 ESD 医生忽视或扩大穿孔。
我们提出了一种使用 GIoU 和高斯亲和度损失的 YOLOv3 训练方法，用于检测和定位结肠镜图像中的穿孔。在这种方法中，目标函数包含广义交并比损失和高斯亲和度损失。我们提出了一种使用所提出的损失函数训练 YOLOv3 架构的方法，以精确地检测和定位穿孔。
为了定性和定量评估所提出的方法，我们从 49 个 ESD 视频中创建了一个数据集。所提出的方法在我们的数据集上的结果显示了穿孔检测和定位的最新性能，达到了 0.881 的准确率、0.869 的 AUC 和 0.879 的平均精度。此外，所提出的方法能够在 0.1 秒内检测到新出现的穿孔。
实验结果表明，所提出的损失函数训练的 YOLOv3 在穿孔检测和定位方面非常有效。所提出的方法可以快速准确地提醒医生 ESD 中发生穿孔。我们相信，通过提出的方法，可以构建一个未来用于临床应用的 CAD 系统。
