117 related articles for article (PubMed ID: 34804142)
21. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.
Ibragimov B; Xing L
Med Phys; 2017 Feb; 44(2):547-557. PubMed ID: 28205307
[TBL] [Abstract][Full Text] [Related]
22. Automatic localization and segmentation of focal cortical dysplasia in FLAIR-negative patients using a convolutional neural network.
Feng C; Zhao H; Li Y; Wen J
J Appl Clin Med Phys; 2020 Sep; 21(9):215-226. PubMed ID: 32809276
[TBL] [Abstract][Full Text] [Related]
23. 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]
24. Dynamic Regulation of Level Set Parameters Using 3D Convolutional Neural Network for Liver Tumor Segmentation.
Deng Z; Guo Q; Zhu Z
J Healthc Eng; 2019; 2019():4321645. PubMed ID: 30918620
[TBL] [Abstract][Full Text] [Related]
25. Deeply self-supervised contour embedded neural network applied to liver segmentation.
Chung M; Lee J; Lee M; Lee J; Shin YG
Comput Methods Programs Biomed; 2020 Aug; 192():105447. PubMed ID: 32203792
[TBL] [Abstract][Full Text] [Related]
26. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.
Fechter T; Adebahr S; Baltas D; Ben Ayed I; Desrosiers C; Dolz J
Med Phys; 2017 Dec; 44(12):6341-6352. PubMed ID: 28940372
[TBL] [Abstract][Full Text] [Related]
27. Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales.
Xu Y; Zhang Y; Bi K; Ning Z; Xu L; Shen M; Deng G; Wang Y
J Digit Imaging; 2020 Oct; 33(5):1155-1166. PubMed ID: 32556913
[TBL] [Abstract][Full Text] [Related]
28. Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach.
Xie L; Song Y; Chen Q
Comput Biol Med; 2020 Jul; 122():103877. PubMed ID: 32658742
[TBL] [Abstract][Full Text] [Related]
29. Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture.
Qin W; Wu Y; Li S; Chen Y; Yang Y; Liu X; Zheng H; Liang D; Hu Z
Biomed Phys Eng Express; 2020 Feb; 6(2):025009. PubMed ID: 33438635
[TBL] [Abstract][Full Text] [Related]
30. Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision.
Kearney V; Chan JW; Wang T; Perry A; Yom SS; Solberg TD
Phys Med Biol; 2019 Jul; 64(13):135001. PubMed ID: 31181561
[TBL] [Abstract][Full Text] [Related]
31. Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network.
Xin S; Shi H; Jide A; Zhu M; Ma C; Liao H
Med Biol Eng Comput; 2020 Mar; 58(3):659-668. PubMed ID: 31950330
[TBL] [Abstract][Full Text] [Related]
32. A segmentation method combining probability map and boundary based on multiple fully convolutional networks and repetitive training.
Yin W; Hu Y; Yi S; He J
Phys Med Biol; 2019 Sep; 64(18):185003. PubMed ID: 30808019
[TBL] [Abstract][Full Text] [Related]
33. Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model.
Hu Y; Guo Y; Wang Y; Yu J; Li J; Zhou S; Chang C
Med Phys; 2019 Jan; 46(1):215-228. PubMed ID: 30374980
[TBL] [Abstract][Full Text] [Related]
34. Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets.
Cha KH; Hadjiiski L; Samala RK; Chan HP; Caoili EM; Cohan RH
Med Phys; 2016 Apr; 43(4):1882. PubMed ID: 27036584
[TBL] [Abstract][Full Text] [Related]
35. Automatic Segmentation of the Paranasal Sinus from Computer Tomography Images Using a Probabilistic Atlas and a Fully Convolutional Network.
Iwamoto Y; Xiong K; Kitamura T; Han XH; Matsushiro N; Nishimura H; Chen YW
Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():2789-2792. PubMed ID: 31946472
[TBL] [Abstract][Full Text] [Related]
36. Fully Automatic Segmentation of Acute Ischemic Lesions on Diffusion-Weighted Imaging Using Convolutional Neural Networks: Comparison with Conventional Algorithms.
Woo I; Lee A; Jung SC; Lee H; Kim N; Cho SJ; Kim D; Lee J; Sunwoo L; Kang DW
Korean J Radiol; 2019 Aug; 20(8):1275-1284. PubMed ID: 31339015
[TBL] [Abstract][Full Text] [Related]
37. Double-branched and area-constraint fully convolutional networks for automated serous retinal detachment segmentation in SD-OCT images.
Gao K; Niu S; Ji Z; Wu M; Chen Q; Xu R; Yuan S; Fan W; Chen Y; Dong J
Comput Methods Programs Biomed; 2019 Jul; 176():69-80. PubMed ID: 31200913
[TBL] [Abstract][Full Text] [Related]
38. DENSE-INception U-net for medical image segmentation.
Zhang Z; Wu C; Coleman S; Kerr D
Comput Methods Programs Biomed; 2020 Aug; 192():105395. PubMed ID: 32163817
[TBL] [Abstract][Full Text] [Related]
39. One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks.
Valverde S; Salem M; Cabezas M; Pareto D; Vilanova JC; Ramió-Torrentà L; Rovira À; Salvi J; Oliver A; Lladó X
Neuroimage Clin; 2019; 21():101638. PubMed ID: 30555005
[TBL] [Abstract][Full Text] [Related]
40. An application of cascaded 3D fully convolutional networks for medical image segmentation.
Roth HR; Oda H; Zhou X; Shimizu N; Yang Y; Hayashi Y; Oda M; Fujiwara M; Misawa K; Mori K
Comput Med Imaging Graph; 2018 Jun; 66():90-99. PubMed ID: 29573583
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]