191 related articles for article (PubMed ID: 30928830)
1. CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.
Wang S; He K; Nie D; Zhou S; Gao Y; Shen D
Med Image Anal; 2019 May; 54():168-178. PubMed ID: 30928830
[TBL] [Abstract][Full Text] [Related]
2. Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images.
Shao Y; Gao Y; Wang Q; Yang X; Shen D
Med Image Anal; 2015 Dec; 26(1):345-56. PubMed ID: 26439938
[TBL] [Abstract][Full Text] [Related]
3. Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks.
He K; Cao X; Shi Y; Nie D; Gao Y; Shen D
IEEE Trans Med Imaging; 2019 Feb; 38(2):585-595. PubMed ID: 30176583
[TBL] [Abstract][Full Text] [Related]
4. Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy.
Wang S; Liu M; Lian J; Shen D
IEEE Trans Med Imaging; 2021 Jan; 40(1):310-320. PubMed ID: 32956051
[TBL] [Abstract][Full Text] [Related]
5. ARPM-net: A novel CNN-based adversarial method with Markov random field enhancement for prostate and organs at risk segmentation in pelvic CT images.
Zhang Z; Zhao T; Gay H; Zhang W; Sun B
Med Phys; 2021 Jan; 48(1):227-237. PubMed ID: 33151620
[TBL] [Abstract][Full Text] [Related]
6. Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests.
Gao Y; Shao Y; Lian J; Wang AZ; Chen RC; Shen D
IEEE Trans Med Imaging; 2016 Jun; 35(6):1532-43. PubMed ID: 26800531
[TBL] [Abstract][Full Text] [Related]
7. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
Tong N; Gou S; Yang S; Ruan D; Sheng K
Med Phys; 2018 Oct; 45(10):4558-4567. PubMed ID: 30136285
[TBL] [Abstract][Full Text] [Related]
8. Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks.
He K; Cao X; Shi Y; Nie D; Gao Y; Shen D
IEEE Trans Med Imaging; 2018 Aug; ():. PubMed ID: 30106714
[TBL] [Abstract][Full Text] [Related]
9. Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT.
Hirashima H; Nakamura M; Baillehache P; Fujimoto Y; Nakagawa S; Saruya Y; Kabasawa T; Mizowaki T
Radiat Oncol; 2021 Jul; 16(1):135. PubMed ID: 34294090
[TBL] [Abstract][Full Text] [Related]
10. Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network.
Dong X; Lei Y; Tian S; Wang T; Patel P; Curran WJ; Jani AB; Liu T; Yang X
Radiother Oncol; 2019 Dec; 141():192-199. PubMed ID: 31630868
[TBL] [Abstract][Full Text] [Related]
11. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion.
Wang Y; Zhou Y; Shen W; Park S; Fishman EK; Yuille AL
Med Image Anal; 2019 Jul; 55():88-102. PubMed ID: 31035060
[TBL] [Abstract][Full Text] [Related]
12. Fully automated multiorgan segmentation of female pelvic magnetic resonance images with coarse-to-fine convolutional neural network.
Zabihollahy F; Viswanathan AN; Schmidt EJ; Morcos M; Lee J
Med Phys; 2021 Nov; 48(11):7028-7042. PubMed ID: 34609756
[TBL] [Abstract][Full Text] [Related]
13. Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation.
Roth HR; Lu L; Lay N; Harrison AP; Farag A; Sohn A; Summers RM
Med Image Anal; 2018 Apr; 45():94-107. PubMed ID: 29427897
[TBL] [Abstract][Full Text] [Related]
14. RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning.
Liu J; Wang C; Guo J; Shao J; Xu X; Liu X; Li H; Li W; Yi Z
Int J Comput Assist Radiol Surg; 2021 Jun; 16(6):895-904. PubMed ID: 33846890
[TBL] [Abstract][Full Text] [Related]
15. Sparse patch based prostate segmentation in CT images.
Liao S; Gao Y; Shen D
Med Image Comput Comput Assist Interv; 2012; 15(Pt 3):385-92. PubMed ID: 23286154
[TBL] [Abstract][Full Text] [Related]
16. Precise segmentation of multiple organs in CT volumes using learning-based approach and information theory.
Lu C; Zheng Y; Birkbeck N; Zhang J; Kohlberger T; Tietjen C; Boettger T; Duncan JS; Zhou SK
Med Image Comput Comput Assist Interv; 2012; 15(Pt 2):462-9. PubMed ID: 23286081
[TBL] [Abstract][Full Text] [Related]
17. Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation.
Zhu Q; Du B; Yan P
IEEE Trans Med Imaging; 2020 Mar; 39(3):753-763. PubMed ID: 31425022
[TBL] [Abstract][Full Text] [Related]
18. A feature-based learning framework for accurate prostate localization in CT images.
Liao S; Shen D
IEEE Trans Image Process; 2012 Aug; 21(8):3546-59. PubMed ID: 22510948
[TBL] [Abstract][Full Text] [Related]
19. Automatic liver segmentation by integrating fully convolutional networks into active contour models.
Guo X; Schwartz LH; Zhao B
Med Phys; 2019 Oct; 46(10):4455-4469. PubMed ID: 31356688
[TBL] [Abstract][Full Text] [Related]
20. Fully automated organ segmentation in male pelvic CT images.
Balagopal A; Kazemifar S; Nguyen D; Lin MH; Hannan R; Owrangi A; Jiang S
Phys Med Biol; 2018 Dec; 63(24):245015. PubMed ID: 30523973
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]