310 related articles for article (PubMed ID: 33496971)
1. Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function.
Tan M; Wu F; Kong D; Mao X
Med Phys; 2021 Apr; 48(4):1707-1719. PubMed ID: 33496971
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Incorporating prior shape knowledge via data-driven loss model to improve 3D liver segmentation in deep CNNs.
Mohagheghi S; Foruzan AH
Int J Comput Assist Radiol Surg; 2020 Feb; 15(2):249-257. PubMed ID: 31686380
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.
Hu P; Wu F; Peng J; Bao Y; Chen F; Kong D
Int J Comput Assist Radiol Surg; 2017 Mar; 12(3):399-411. PubMed ID: 27885540
[TBL] [Abstract][Full Text] [Related]
6. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution.
Hu P; Wu F; Peng J; Liang P; Kong D
Phys Med Biol; 2016 Dec; 61(24):8676-8698. PubMed ID: 27880735
[TBL] [Abstract][Full Text] [Related]
7. Automatic 3D liver location and segmentation via convolutional neural network and graph cut.
Lu F; Wu F; Hu P; Peng Z; Kong D
Int J Comput Assist Radiol Surg; 2017 Feb; 12(2):171-182. PubMed ID: 27604760
[TBL] [Abstract][Full Text] [Related]
8. Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging.
Qayyum A; Lalande A; Meriaudeau F
Comput Biol Med; 2020 Dec; 127():104097. PubMed ID: 33142142
[TBL] [Abstract][Full Text] [Related]
9. Learning fuzzy clustering for SPECT/CT segmentation via convolutional neural networks.
Chen J; Li Y; Luna LP; Chung HW; Rowe SP; Du Y; Solnes LB; Frey EC
Med Phys; 2021 Jul; 48(7):3860-3877. PubMed ID: 33905560
[TBL] [Abstract][Full Text] [Related]
10. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images.
Wang J; Cheng Y; Guo C; Wang Y; Tamura S
Int J Comput Assist Radiol Surg; 2016 May; 11(5):817-26. PubMed ID: 26646416
[TBL] [Abstract][Full Text] [Related]
11. Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation.
Qin W; Wu J; Han F; Yuan Y; Zhao W; Ibragimov B; Gu J; Xing L
Phys Med Biol; 2018 May; 63(9):095017. PubMed ID: 29633960
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Liver tumor segmentation based on 3D convolutional neural network with dual scale.
Meng L; Tian Y; Bu S
J Appl Clin Med Phys; 2020 Jan; 21(1):144-157. PubMed ID: 31793212
[TBL] [Abstract][Full Text] [Related]
14. Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.
Liu X; Guo S; Yang B; Ma S; Zhang H; Li J; Sun C; Jin L; Li X; Yang Q; Fu Y
J Digit Imaging; 2018 Oct; 31(5):748-760. PubMed ID: 29679242
[TBL] [Abstract][Full Text] [Related]
15. Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.
Tong N; Gou S; Yang S; Cao M; Sheng K
Med Phys; 2019 Jun; 46(6):2669-2682. PubMed ID: 31002188
[TBL] [Abstract][Full Text] [Related]
16. MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images.
Li W; Qin S; Li F; Wang L
Med Phys; 2021 Jan; 48(1):329-341. PubMed ID: 33222222
[TBL] [Abstract][Full Text] [Related]
17. Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks.
Conze PH; Kavur AE; Cornec-Le Gall E; Gezer NS; Le Meur Y; Selver MA; Rousseau F
Artif Intell Med; 2021 Jul; 117():102109. PubMed ID: 34127239
[TBL] [Abstract][Full Text] [Related]
18. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.
He B; Huang C; Sharp G; Zhou S; Hu Q; Fang C; Fan Y; Jia F
Med Phys; 2016 May; 43(5):2421. PubMed ID: 27147353
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour.
Jin R; Wang M; Xu L; Lu J; Song E; Ma G
Med Phys; 2023 Apr; 50(4):2100-2120. PubMed ID: 36413182
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]