Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images-Reference-Cited by-同舟云学术

Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images

Published:2022 Issue: Volume: Page:109-120
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

De Biase Alessia^ORCID,Tang Wei^ORCID,Sourlos Nikos^ORCID,Ma Baoqiang^ORCID,Guo Jiapan^ORCID,Sijtsema Nanna Maria^ORCID,van Ooijen Peter^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-98253-9_10

Reference10 articles.

1. Xue, Z., et al.: Multi-modal co-learning for liver lesion segmentation on PET-CT images. IEEE Trans. Med. Imaging. https://doi.org/10.1109/TMI.2021.3089702

2. Chow, L.Q.M.: Head and Neck Cancer. N Engl. J. Med. 382(1), 60–72 (2020). PMID: 31893516. https://doi.org/10.1056/NEJMra1715715

3. Yeh, S.A.: Radiotherapy for head and neck cancer. Semin. Plast. Surg. 24(2), 127–136 (2010). https://doi.org/10.1055/s-0030-1255330

4. Gudi, S., et al.: Interobserver variability in the delineation of gross tumour volume and specified organs-at-risk during IMRT for head and neck cancers and the impact of FDG-PET/CT on such variability at the primary site. J. Med. Imaging Radiat. Sci. 48(2), 184–192 (2017)

5. Andrearczyk, V., et al.: Automatic segmentation of head and neck tumors and nodal metastases in PET-CT scans. In: Medical Imaging with Deep Learning (MIDL) (2020)

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