Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network-Reference-Cited by-同舟云学术

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Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network

Published:2023-04-21 Issue:9 Volume:61 Page:2379-2389
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Dou Meng,Chen Zhebin,Tang Yuanling,Sheng Leiming,Zhou Jitao,Wang Xin,Yao Yu

Funder

National Natural Science Foundation of China

Sichuan Province Science and Technology Support Program

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-023-02828-9.pdf

Reference24 articles.

1. Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660

2. Feng L, Liu Z, Li C et al (2022) Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit Health 4:e8–e17. https://doi.org/10.1016/S2589-7500(21)00215-6

3. Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA A Cancer J Clin caac.21552. https://doi.org/10.3322/caac.21552

4. Shin J, Seo N, Baek S-E et al (2022) MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy. Radiol 211986. https://doi.org/10.1148/radiol.211986

5. van Heeswijk (2017) Novel MR techniques in rectal cancer: translating research into practice. Maastricht University

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning for MRI lesion segmentation in rectal cancer;Frontiers in Medicine;2024-06-25

2. Magnetic resonance image segmentation of rectal tumors based on improved CycleGAN and U-Net models;Multimedia Tools and Applications;2023-09-21

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