Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer-Reference-Cited by-同舟云学术

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Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

Published:2019-11-11 Issue:3 Volume:30 Page:1297-1305
ISSN:0938-7994
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Lin Yu-Chun,Lin Chia-Hung,Lu Hsin-Ying,Chiang Hsin-Ju,Wang Ho-Kai,Huang Yu-Ting,Ng Shu-Hang,Hong Ji-Hong,Yen Tzu-Chen,Lai Chyong-Huey,Lin Gigin^ORCID

Funder

Chang Gung Medical Foundation

Chang Gung Medical Fundation

Ministry of Science and Technology

Publisher

Springer Science and Business Media LLC

Subject

Radiology Nuclear Medicine and imaging,General Medicine

Link

http://link.springer.com/content/pdf/10.1007/s00330-019-06467-3.pdf

Reference26 articles.

1. Sala E, Rockall AG, Freeman SJ, Mitchell DG, Reinhold C (2013) The added role of MR imaging in treatment stratification of patients with gynecologic malignancies: what the radiologist needs to know. Radiology 266:717–740

2. Bhatla N, Berek JS, Cuello Fredes M et al (2019) Revised FIGO staging for carcinoma of the cervix uteri. Int J Gynaecol Obstet 145:129–135

3. Ma DJ, Zhu JM, Grigsby PW (2011) Tumor volume discrepancies between FDG-PET and MRI for cervical cancer. Radiother Oncol 98:139–142

4. Dimopoulos JC, De Vos V, Berger D et al (2009) Inter-observer comparison of target delineation for MRI-assisted cervical cancer brachytherapy: application of the GYN GEC-ESTRO recommendations. Radiother Oncol 91:166–172

5. Lin YC, Lin G, Hong JH et al (2017) Diffusion radiomics analysis of intratumoral heterogeneity in a murine prostate cancer model following radiotherapy: Pixelwise correlation with histology. J Magn Reson Imaging 46:483–489

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