The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations-Reference-Cited by-同舟云学术

The Segmentation of Multiple Types of Uterine Lesions in Magnetic Resonance Images Using a Sequential Deep Learning Method with Image-Level Annotations

Published:2024-01-16 Issue: Volume: Page:
ISSN:2948-2933
Container-title:Journal of Imaging Informatics in Medicine
language:en
Short-container-title:J Digit Imaging. Inform. med.

Author:

Cui Yu-meng,Wang Hua-li,Cao Rui,Bai Hong,Sun Dan,Feng Jiu-xiang,Lu Xue-feng

Funder

National Natural Science Foundation of China

Natural Science Foundation of Liaoning Province

Dalian Medical Science Research Program

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10278-023-00931-9.pdf

Reference25 articles.

1. Berek JS, Matias-Guiu X, Creutzberg C, Fotopoulou C, Gaffney D, Kehoe S, Lindemann K, Mutch D, Concin N; Endometrial Cancer Staging Subcommittee, FIGO Women's Cancer Committee. FIGO staging of endometrial cancer: 2023. Int J Gynaecol Obstet. 2023; 162 (2): 383–394. https://doi.org/10.1002/ijgo.14923.

2. Rahimpour M, Saint Martin MJ, Frouin F, Akl P, Orlhac F, Koole M, Malhaire C. Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI. Eur Radiol. 2023; 33 (2): 959-969. https://doi.org/10.1007/s00330-022-09113-7.

3. Opfer R, Krüger J, Spies L, Ostwaldt AC, Kitzler HH, Schippling S, Buchert R. Automatic segmentation of the thalamus using a massively trained 3D convolutional neural network: higher sensitivity for the detection of reduced thalamus volume by improved inter-scanner stability. Eur Radiol. 2023; 33 (3): 1852-1861. https://doi.org/10.1007/s00330-022-09170-y.

4. Chen C, Zhang T, Teng Y, Yu Y, Shu X, Zhang L, Zhao F, Xu J. Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network. Eur Radiol. 2023; 33 (4): 2665-2675. https://doi.org/10.1007/s00330-022-09216-1.

5. Jávorszky N, Homonnay B, Gerstenblith G, Bluemke D, Kiss P, Török M, Celentano D, Lai H, Lai S, Kolossváry M. Deep learning-based atherosclerotic coronary plaque segmentation on coronary CT angiography. Eur Radiol. 2022; 32 (10): 7217-7226. https://doi.org/10.1007/s00330-022-08801-8.