A mucosal recovery software tool for endoscopic submucosal dissection in early gastric cancer

Background Due to the limited diagnostic ability, the low detection rate of early gastric cancer (EGC) is a serious health threat. The establishment of the mapping between endoscopic images and pathological images can rapidly improve the diagnostic ability to detect EGC. To expedite the learning process of EGC diagnosis, a mucosal recovery map for the mapping between ESD mucosa specimen and pathological images should be performed in collaboration with endoscopists and pathologists, which is a time-consuming and laborious work. Methods 20 patients at the Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College from March 2020 to July 2020 were enrolled in this study. We proposed the improved U-Net to obtain WSI-level segmentation results, and the WSI-level results can be mapped to the macroscopic image of the specimen. For the convenient use, a software pipeline named as “Pathology Helper” for integration the workflow of the construction of mucosal recovery maps was developed. Results The MIoU and Dice of our model can achieve 0.955 ± 0.0936 and 0.961 ± 0.0874 for WSI-level segmentation, respectively. With the help of “Pathology Helper”, we can construct the high-quality mucosal recovery maps to reduce the workload of endoscopists and pathologists. Conclusion “Pathology Helper” will accelerate the learning of endoscopists and pathologists, and rapidly improve their abilities to detect EGC. Our work can also improve the detection rate of early gastric cancer, so that more patients with gastric cancer will be treated in a timely manner.

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