129 related articles for article (PubMed ID: 38613896)
1. Tumor conspicuity enhancement-based segmentation model for liver tumor segmentation and RECIST diameter measurement in non-contrast CT images.
Liu H; Zhou Y; Gou S; Luo Z
Comput Biol Med; 2024 May; 174():108420. PubMed ID: 38613896
[TBL] [Abstract][Full Text] [Related]
2. Deep learning from dual-energy information for whole-heart segmentation in dual-energy and single-energy non-contrast-enhanced cardiac CT.
Bruns S; Wolterink JM; Takx RAP; van Hamersvelt RW; Suchá D; Viergever MA; Leiner T; Išgum I
Med Phys; 2020 Oct; 47(10):5048-5060. PubMed ID: 32786071
[TBL] [Abstract][Full Text] [Related]
3. Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration.
Luu MH; Mai HS; Pham XL; Le QA; Le QK; Walsum TV; Le NH; Franklin D; Le VH; Moelker A; Chu DT; Trung NL
Comput Methods Programs Biomed; 2023 May; 233():107453. PubMed ID: 36921463
[TBL] [Abstract][Full Text] [Related]
4. Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation.
Lee SB; Hong Y; Cho YJ; Jeong D; Lee J; Yoon SH; Lee S; Choi YH; Cheon JE
Korean J Radiol; 2023 Apr; 24(4):294-304. PubMed ID: 36907592
[TBL] [Abstract][Full Text] [Related]
5. HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images.
Kushnure DT; Talbar SN
Comput Methods Programs Biomed; 2022 Jan; 213():106501. PubMed ID: 34752959
[TBL] [Abstract][Full Text] [Related]
6. Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT.
He K; Liu X; Shahzad R; Reimer R; Thiele F; Niehoff J; Wybranski C; Bunck AC; Zhang H; Perkuhn M
Front Oncol; 2021; 11():669437. PubMed ID: 34336661
[TBL] [Abstract][Full Text] [Related]
7. NCCT-CECT image synthesizers and their application to pulmonary vessel segmentation.
Pang H; Qi S; Wu Y; Wang M; Li C; Sun Y; Qian W; Tang G; Xu J; Liang Z; Chen R
Comput Methods Programs Biomed; 2023 Apr; 231():107389. PubMed ID: 36739625
[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. A study of generalization and compatibility performance of 3D U-Net segmentation on multiple heterogeneous liver CT datasets.
He B; Yin D; Chen X; Luo H; Xiao D; He M; Wang G; Fang C; Liu L; Jia F
BMC Med Imaging; 2021 Nov; 21(1):178. PubMed ID: 34819022
[TBL] [Abstract][Full Text] [Related]
10. Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.
Ouhmich F; Agnus V; Noblet V; Heitz F; Pessaux P
Int J Comput Assist Radiol Surg; 2019 Aug; 14(8):1275-1284. PubMed ID: 31041697
[TBL] [Abstract][Full Text] [Related]
11. A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy.
Li Z; Zhu Q; Zhang L; Yang X; Li Z; Fu J
Radiat Oncol; 2022 Sep; 17(1):152. PubMed ID: 36064571
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.
Tang F; Liang S; Zhong T; Huang X; Deng X; Zhang Y; Zhou L
Eur Radiol; 2020 Feb; 30(2):823-832. PubMed ID: 31650265
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. Generating synthetic contrast enhancement from non-contrast chest computed tomography using a generative adversarial network.
Choi JW; Cho YJ; Ha JY; Lee SB; Lee S; Choi YH; Cheon JE; Kim WS
Sci Rep; 2021 Oct; 11(1):20403. PubMed ID: 34650076
[TBL] [Abstract][Full Text] [Related]
17. Segmentation of liver tumors with abdominal computed tomography using fully convolutional networks.
Chen CI; Lu NH; Huang YH; Liu KY; Hsu SY; Matsushima A; Wang YM; Chen TB
J Xray Sci Technol; 2022; 30(5):953-966. PubMed ID: 35754254
[TBL] [Abstract][Full Text] [Related]
18. PA-Net: A phase attention network fusing venous and arterial phase features of CT images for liver tumor segmentation.
Liu Z; Hou J; Pan X; Zhang R; Shi Z
Comput Methods Programs Biomed; 2024 Feb; 244():107997. PubMed ID: 38176329
[TBL] [Abstract][Full Text] [Related]
19. Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.
Lin YC; Lin G; Pandey S; Yeh CH; Wang JJ; Lin CY; Ho TY; Ko SF; Ng SH
Eur Radiol; 2023 Sep; 33(9):6548-6556. PubMed ID: 37338554
[TBL] [Abstract][Full Text] [Related]
20. Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography.
Abbani N; Baudier T; Rit S; Franco FD; Okoli F; Jaouen V; Tilquin F; Barateau A; Simon A; de Crevoisier R; Bert J; Sarrut D
Med Phys; 2022 Nov; 49(11):6930-6944. PubMed ID: 36000762
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
