260 related articles for article (PubMed ID: 30159326)
1. A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images.
Zheng Z; Zhang X; Xu H; Liang W; Zheng S; Shi Y
Biomed Res Int; 2018; 2018():3815346. PubMed ID: 30159326
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Blood vessel-based liver segmentation using the portal phase of an abdominal CT dataset.
Maklad AS; Matsuhiro M; Suzuki H; Kawata Y; Niki N; Satake M; Moriyama N; Utsunomiya T; Shimada M
Med Phys; 2013 Nov; 40(11):113501. PubMed ID: 24320472
[TBL] [Abstract][Full Text] [Related]
5. 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy
Wu W; Wu S; Zhou Z; Zhang R; Zhang Y
Biomed Res Int; 2017; 2017():5207685. PubMed ID: 29090220
[TBL] [Abstract][Full Text] [Related]
6. A variational approach to liver segmentation using statistics from multiple sources.
Zheng S; Fang B; Li L; Gao M; Wang Y
Phys Med Biol; 2018 Jan; 63(2):025024. PubMed ID: 29265012
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images.
Seo H; Huang C; Bassenne M; Xiao R; Xing L
IEEE Trans Med Imaging; 2020 May; 39(5):1316-1325. PubMed ID: 31634827
[TBL] [Abstract][Full Text] [Related]
9. An Improved Fuzzy Connectedness Method for Automatic Three-Dimensional Liver Vessel Segmentation in CT Images.
Zhang R; Zhou Z; Wu W; Lin CC; Tsui PH; Wu S
J Healthc Eng; 2018; 2018():2376317. PubMed ID: 30510670
[TBL] [Abstract][Full Text] [Related]
10. Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation.
Budak Ü; Guo Y; Tanyildizi E; Şengür A
Med Hypotheses; 2020 Jan; 134():109431. PubMed ID: 31669758
[TBL] [Abstract][Full Text] [Related]
11. Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation.
Häme Y; Pollari M
Med Image Anal; 2012 Jan; 16(1):140-9. PubMed ID: 21742543
[TBL] [Abstract][Full Text] [Related]
12. Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images.
Li G; Chen X; Shi F; Zhu W; Tian J; Xiang D
IEEE Trans Image Process; 2015 Dec; 24(12):5315-29. PubMed ID: 26415173
[TBL] [Abstract][Full Text] [Related]
13. Computer-assisted liver tumor surgery using a novel semiautomatic and a hybrid semiautomatic segmentation algorithm.
Zygomalas A; Karavias D; Koutsouris D; Maroulis I; Karavias DD; Giokas K; Megalooikonomou V
Med Biol Eng Comput; 2016 May; 54(5):711-21. PubMed ID: 26307199
[TBL] [Abstract][Full Text] [Related]
14. SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography.
Wang J; Lv P; Wang H; Shi C
Comput Methods Programs Biomed; 2021 Sep; 208():106268. PubMed ID: 34274611
[TBL] [Abstract][Full Text] [Related]
15. Efficient liver segmentation in CT images based on graph cuts and bottleneck detection.
Liao M; Zhao YQ; Wang W; Zeng YZ; Yang Q; Shih FY; Zou BJ
Phys Med; 2016 Nov; 32(11):1383-1396. PubMed ID: 27771278
[TBL] [Abstract][Full Text] [Related]
16. Global optimal hybrid geometric active contour for automated lung segmentation on CT images.
Zhang W; Wang X; Zhang P; Chen J
Comput Biol Med; 2017 Dec; 91():168-180. PubMed ID: 29080491
[TBL] [Abstract][Full Text] [Related]
17. Semiautomatic segmentation of liver metastases on volumetric CT images.
Yan J; Schwartz LH; Zhao B
Med Phys; 2015 Nov; 42(11):6283-93. PubMed ID: 26520721
[TBL] [Abstract][Full Text] [Related]
18. Liver segmentation from abdominal CT volumes based on level set and sparse shape composition.
Li Y; Zhao YQ; Zhang F; Liao M; Yu LL; Chen BF; Wang YJ
Comput Methods Programs Biomed; 2020 Oct; 195():105533. PubMed ID: 32502932
[TBL] [Abstract][Full Text] [Related]
19. CTumorGAN: a unified framework for automatic computed tomography tumor segmentation.
Pang S; Du A; Orgun MA; Yu Z; Wang Y; Wang Y; Liu G
Eur J Nucl Med Mol Imaging; 2020 Sep; 47(10):2248-2268. PubMed ID: 32222809
[TBL] [Abstract][Full Text] [Related]
20. 3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images.
Salman Al-Shaikhli SD; Yang MY; Rosenhahn B
Biomed Tech (Berl); 2016 Aug; 61(4):401-12. PubMed ID: 26501155
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
