485 related articles for article (PubMed ID: 34224147)
1. An effective deep network for automatic segmentation of complex lung tumors in CT images.
Wang B; Chen K; Tian X; Yang Y; Zhang X
Med Phys; 2021 Sep; 48(9):5004-5016. PubMed ID: 34224147
[TBL] [Abstract][Full Text] [Related]
2. PA-ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images.
Xu Y; Cai M; Lin L; Zhang Y; Hu H; Peng Z; Zhang Q; Chen Q; Mao X; Iwamoto Y; Han XH; Chen YW; Tong R
Med Phys; 2021 Jul; 48(7):3752-3766. PubMed ID: 33950526
[TBL] [Abstract][Full Text] [Related]
3. Lung tumor segmentation in 4D CT images using motion convolutional neural networks.
Momin S; Lei Y; Tian Z; Wang T; Roper J; Kesarwala AH; Higgins K; Bradley JD; Liu T; Yang X
Med Phys; 2021 Nov; 48(11):7141-7153. PubMed ID: 34469001
[TBL] [Abstract][Full Text] [Related]
4. Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net.
Zhang G; Yang Z; Huo B; Chai S; Jiang S
Comput Methods Programs Biomed; 2021 Nov; 211():106419. PubMed ID: 34563895
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. A multiple-channel and atrous convolution network for ultrasound image segmentation.
Zhang L; Zhang J; Li Z; Song Y
Med Phys; 2020 Dec; 47(12):6270-6285. PubMed ID: 33007105
[TBL] [Abstract][Full Text] [Related]
7. Boundary Aware Semantic Segmentation using Pyramid-dilated Dense U-Net for Lung Segmentation in Computed Tomography Images.
Agnes SA
J Med Phys; 2023; 48(2):161-174. PubMed ID: 37576094
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Automatic segmentation of organs-at-risks of nasopharynx cancer and lung cancer by cross-layer attention fusion network with TELD-Loss.
Liu Z; Sun C; Wang H; Li Z; Gao Y; Lei W; Zhang S; Wang G; Zhang S
Med Phys; 2021 Nov; 48(11):6987-7002. PubMed ID: 34608652
[TBL] [Abstract][Full Text] [Related]
10. A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.
Kunkyab T; Bahrami Z; Zhang H; Liu Z; Hyde D
J Appl Clin Med Phys; 2024 Mar; 25(3):e14297. PubMed ID: 38373289
[TBL] [Abstract][Full Text] [Related]
11. ABCNet: A new efficient 3D dense-structure network for segmentation and analysis of body tissue composition on body-torso-wide CT images.
Liu T; Pan J; Torigian DA; Xu P; Miao Q; Tong Y; Udupa JK
Med Phys; 2020 Jul; 47(7):2986-2999. PubMed ID: 32170754
[TBL] [Abstract][Full Text] [Related]
12. Vessel segmentation from volumetric images: a multi-scale double-pathway network with class-balanced loss at the voxel level.
Chen Y; Fan S; Chen Y; Che C; Cao X; He X; Song X; Zhao F
Med Phys; 2021 Jul; 48(7):3804-3814. PubMed ID: 33969487
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. HMA-Net: A deep U-shaped network combined with HarDNet and multi-attention mechanism for medical image segmentation.
Liu Q; Han Z; Liu Z; Zhang J
Med Phys; 2023 Mar; 50(3):1635-1646. PubMed ID: 36303466
[TBL] [Abstract][Full Text] [Related]
15. Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network.
Gan W; Wang H; Gu H; Duan Y; Shao Y; Chen H; Feng A; Huang Y; Fu X; Ying Y; Quan H; Xu Z
Br J Radiol; 2021 Oct; 94(1126):20210038. PubMed ID: 34347535
[TBL] [Abstract][Full Text] [Related]
16. Multi-scale segmentation squeeze-and-excitation UNet with conditional random field for segmenting lung tumor from CT images.
Zhang B; Qi S; Wu Y; Pan X; Yao Y; Qian W; Guan Y
Comput Methods Programs Biomed; 2022 Jul; 222():106946. PubMed ID: 35716533
[TBL] [Abstract][Full Text] [Related]
17. Automatic lung tumor segmentation from CT images using improved 3D densely connected UNet.
Zhang G; Yang Z; Jiang S
Med Biol Eng Comput; 2022 Nov; 60(11):3311-3323. PubMed ID: 36169904
[TBL] [Abstract][Full Text] [Related]
18. MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT.
Yang J; Wu B; Li L; Cao P; Zaiane O
Comput Med Imaging Graph; 2021 Sep; 92():101957. PubMed ID: 34325225
[TBL] [Abstract][Full Text] [Related]
19. Spline curve deformation model with prior shapes for identifying adhesion boundaries between large lung tumors and tissues around lungs in CT images.
Zhang X; Wang J; Yang Y; Wang B; Gu L
Med Phys; 2020 Mar; 47(3):1011-1020. PubMed ID: 31883391
[TBL] [Abstract][Full Text] [Related]
20. Automatic mandible segmentation from CT image using 3D fully convolutional neural network based on DenseASPP and attention gates.
Xu J; Liu J; Zhang D; Zhou Z; Jiang X; Zhang C; Chen X
Int J Comput Assist Radiol Surg; 2021 Oct; 16(10):1785-1794. PubMed ID: 34287750
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
