Affiliations: [a] School of Software, Nanchang University, Nanchang, Jiangxi, China | [b] Jiangxi Cancer Hospital, Nanchang, Jiangxi, China
Correspondence:
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Corresponding author: Bo Yi, Director of Abdominal Surgery, Cancer Hospital, No. 519 East Beijing Road, Nanchang City, Jiangxi, China. E-mail: [email protected].
Note: [1] These authors contributed equally to this work.
Abstract: BACKGROUND: Rapid and accurate segmentation of tumor regions from rectal cancer images can better understand the patient’s lesions and surrounding tissues, providing more effective auxiliary diagnostic information. However, cutting rectal tumors with deep learning still cannot be compared with manual segmentation, and a major obstacle to cutting rectal tumors with deep learning is the lack of high-quality data sets. OBJECTIVE: We propose to use our Re-segmentation Method to manually correct the model segmentation area and put it into training and training ideas. The data set has been made publicly available. Methods: A total of 354 rectal cancer CT images and 308 rectal region images labeled by experts from Jiangxi Cancer Hospital were included in the data set. Six network architectures are used to train the data set, and the region predicted by the model is manually revised and then put into training to improve the ability of model segmentation and then perform performance measurement. RESULTS: In this study, we use the Resegmentation Method for various popular network architectures. CONCLUSION: By comparing the evaluation indicators before and after using the Re-segmentation Method, we prove that our proposed Re-segmentation Method can further improve the performance of the rectal cancer image segmentation model.
Keywords: Medical image, rectal cancer, data set, semantic segmentation
