121 related articles for article (PubMed ID: 38517809)
1. The effect of the re-segmentation method on improving the performance of rectal cancer image segmentation models.
Lei J; Huang Y; Chen Y; Xia L; Yi B
Technol Health Care; 2024; 32(3):1629-1640. PubMed ID: 38517809
[TBL] [Abstract][Full Text] [Related]
2. Automatic segmentation of rectal tumor on diffusion-weighted images by deep learning with U-Net.
Zhu HT; Zhang XY; Shi YJ; Li XT; Sun YS
J Appl Clin Med Phys; 2021 Sep; 22(9):324-331. PubMed ID: 34343402
[TBL] [Abstract][Full Text] [Related]
3. A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.
Liu Y; Zhang M; Zhong Z; Zeng X
Med Phys; 2023 Mar; 50(3):1528-1538. PubMed ID: 36057788
[TBL] [Abstract][Full Text] [Related]
4. Improved U-Net based on contour prediction for efficient segmentation of rectal cancer.
Li D; Chu X; Cui Y; Zhao J; Zhang K; Yang X
Comput Methods Programs Biomed; 2022 Jan; 213():106493. PubMed ID: 34749245
[TBL] [Abstract][Full Text] [Related]
5. Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy.
Men K; Boimel P; Janopaul-Naylor J; Zhong H; Huang M; Geng H; Cheng C; Fan Y; Plastaras JP; Ben-Josef E; Xiao Y
Phys Med Biol; 2018 Sep; 63(18):185016. PubMed ID: 30109986
[TBL] [Abstract][Full Text] [Related]
6. Imaging segmentation mechanism for rectal tumors using improved U-Net.
Zhang K; Yang X; Cui Y; Zhao J; Li D
BMC Med Imaging; 2024 Apr; 24(1):95. PubMed ID: 38654162
[TBL] [Abstract][Full Text] [Related]
7. Dual parallel net: A novel deep learning model for rectal tumor segmentation via CNN and transformer with Gaussian Mixture prior.
Zhang H; Yang X; Li D; Cui Y; Zhao J; Qiu S
J Biomed Inform; 2023 Mar; 139():104304. PubMed ID: 36736447
[TBL] [Abstract][Full Text] [Related]
8. A medical image segmentation method for rectal tumors based on multi-scale feature retention and multiple attention mechanisms.
Zhao J; Liu L; Yang X; Cui Y; Li D; Zhang H; Zhang K
Med Phys; 2024 May; 51(5):3275-3291. PubMed ID: 38569054
[TBL] [Abstract][Full Text] [Related]
9. The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer.
Guo H; Wang J; Xia X; Zhong Y; Peng J; Zhang Z; Hu W
Radiat Oncol; 2021 Jun; 16(1):113. PubMed ID: 34162410
[TBL] [Abstract][Full Text] [Related]
10. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.
Men K; Dai J; Li Y
Med Phys; 2017 Dec; 44(12):6377-6389. PubMed ID: 28963779
[TBL] [Abstract][Full Text] [Related]
11. Three-stage segmentation of lung region from CT images using deep neural networks.
Osadebey M; Andersen HK; Waaler D; Fossaa K; Martinsen ACT; Pedersen M
BMC Med Imaging; 2021 Jul; 21(1):112. PubMed ID: 34266391
[TBL] [Abstract][Full Text] [Related]
12. Improving segmentation and classification of renal tumors in small sample 3D CT images using transfer learning with convolutional neural networks.
Zhu XL; Shen HB; Sun H; Duan LX; Xu YY
Int J Comput Assist Radiol Surg; 2022 Jul; 17(7):1303-1311. PubMed ID: 35290645
[TBL] [Abstract][Full Text] [Related]
13. A quality assurance framework for routine monitoring of deep learning cardiac substructure computed tomography segmentation models in radiotherapy.
Jin X; Hao Y; Hilliard J; Zhang Z; Thomas MA; Li H; Jha AK; Hugo GD
Med Phys; 2024 Apr; 51(4):2741-2758. PubMed ID: 38015793
[TBL] [Abstract][Full Text] [Related]
14. AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.
Zhu W; Huang Y; Zeng L; Chen X; Liu Y; Qian Z; Du N; Fan W; Xie X
Med Phys; 2019 Feb; 46(2):576-589. PubMed ID: 30480818
[TBL] [Abstract][Full Text] [Related]
15. Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images.
Zhuang M; Chen Z; Wang H; Tang H; He J; Qin B; Yang Y; Jin X; Yu M; Jin B; Li T; Kettunen L
Int J Comput Assist Radiol Surg; 2023 Feb; 18(2):379-394. PubMed ID: 36048319
[TBL] [Abstract][Full Text] [Related]
16. Fast interactive medical image segmentation with weakly supervised deep learning method.
Girum KB; Créhange G; Hussain R; Lalande A
Int J Comput Assist Radiol Surg; 2020 Sep; 15(9):1437-1444. PubMed ID: 32653985
[TBL] [Abstract][Full Text] [Related]
17. [Study on the accuracy of automatic segmentation of knee CT images based on deep learning].
Song P; Fan Z; Zhi X; Cao Z; Min S; Liu X; Zhang Y; Kong X; Chai W
Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi; 2022 May; 36(5):534-539. PubMed ID: 35570625
[TBL] [Abstract][Full Text] [Related]
18. DENSE-INception U-net for medical image segmentation.
Zhang Z; Wu C; Coleman S; Kerr D
Comput Methods Programs Biomed; 2020 Aug; 192():105395. PubMed ID: 32163817
[TBL] [Abstract][Full Text] [Related]
19. Full convolutional network based multiple side-output fusion architecture for the segmentation of rectal tumors in magnetic resonance images: A multi-vendor study.
Wang M; Xie P; Ran Z; Jian J; Zhang R; Xia W; Yu T; Ni C; Gu J; Gao X; Meng X
Med Phys; 2019 Jun; 46(6):2659-2668. PubMed ID: 30972763
[TBL] [Abstract][Full Text] [Related]
20. CAM-Wnet: An effective solution for accurate pulmonary embolism segmentation.
Liu Z; Yuan H; Wang H
Med Phys; 2022 Aug; 49(8):5294-5303. PubMed ID: 35609213
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
