Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

"our source code is available via github ( https://github com/post-ncrt/segmentation-of-rectal-cancer ) and can be coordinated with the nnunet code."

"Declarations Ethics approval and consent to participateAll the use of human data and the experiments were performed in accordance with relevant guidelines and regulations of the Declaration of Helsinki. The study was approved by the medical ethics committee of Peking University Hospital & Institute (ethic code: 2020KT03), and the need for written informed consent was waived by the medical ethics committee of Peking University Hospital & Institute due to retrospective nature of the study. Consent for publicationNot applicable. Competing interestsThe authors declare no competing interests. Competing interests The authors declare no competing interests."

"Funding The study was supported by the National Natural Science Foundation of China (82271955) and Science Foundation of Peking University Cancer Hospital (XKFZ2403)."