161 related articles for article (PubMed ID: 30712604)
1. Spinal cord detection in planning CT for radiotherapy through adaptive template matching, IMSLIC and convolutional neural networks.
Diniz JOB; Diniz PHB; Valente TLA; Silva AC; Paiva AC
Comput Methods Programs Biomed; 2019 Mar; 170():53-67. PubMed ID: 30712604
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.
Diniz PHB; Valente TLA; Diniz JOB; Silva AC; Gattass M; Ventura N; Muniz BC; Gasparetto EL
Comput Methods Programs Biomed; 2018 Dec; 167():49-63. PubMed ID: 29706405
[TBL] [Abstract][Full Text] [Related]
4. Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.
Zhu J; Zhang J; Qiu B; Liu Y; Liu X; Chen L
Acta Oncol; 2019 Feb; 58(2):257-264. PubMed ID: 30398090
[TBL] [Abstract][Full Text] [Related]
5. Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.
Feng X; Qing K; Tustison NJ; Meyer CH; Chen Q
Med Phys; 2019 May; 46(5):2169-2180. PubMed ID: 30830685
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.
Men K; Dai J; Li Y
Med Phys; 2017 Dec; 44(12):6377-6389. PubMed ID: 28963779
[TBL] [Abstract][Full Text] [Related]
8. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.
Liang S; Tang F; Huang X; Yang K; Zhong T; Hu R; Liu S; Yuan X; Zhang Y
Eur Radiol; 2019 Apr; 29(4):1961-1967. PubMed ID: 30302589
[TBL] [Abstract][Full Text] [Related]
9. Esophagus segmentation from planning CT images using an atlas-based deep learning approach.
Diniz JOB; Ferreira JL; Diniz PHB; Silva AC; de Paiva AC
Comput Methods Programs Biomed; 2020 Dec; 197():105685. PubMed ID: 32798976
[TBL] [Abstract][Full Text] [Related]
10. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.
Bandeira Diniz JO; Bandeira Diniz PH; Azevedo Valente TL; Corrêa Silva A; de Paiva AC; Gattass M
Comput Methods Programs Biomed; 2018 Mar; 156():191-207. PubMed ID: 29428071
[TBL] [Abstract][Full Text] [Related]
11. Artificial Intelligence Radiotherapy Planning: Automatic Segmentation of Human Organs in CT Images Based on a Modified Convolutional Neural Network.
Shen G; Jin X; Sun C; Li Q
Front Public Health; 2022; 10():813135. PubMed ID: 35493368
[TBL] [Abstract][Full Text] [Related]
12. Accurate and rapid CT image segmentation of the eyes and surrounding organs for precise radiotherapy.
Sun Y; Shi H; Zhang S; Wang P; Zhao W; Zhou X; Yuan K
Med Phys; 2019 May; 46(5):2214-2222. PubMed ID: 30815885
[TBL] [Abstract][Full Text] [Related]
13. Kidney segmentation from computed tomography images using deep neural network.
da Cruz LB; Araújo JDL; Ferreira JL; Diniz JOB; Silva AC; de Almeida JDS; de Paiva AC; Gattass M
Comput Biol Med; 2020 Aug; 123():103906. PubMed ID: 32768047
[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. AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.
Zhu W; Huang Y; Zeng L; Chen X; Liu Y; Qian Z; Du N; Fan W; Xie X
Med Phys; 2019 Feb; 46(2):576-589. PubMed ID: 30480818
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.
Teramoto A; Fujita H; Yamamuro O; Tamaki T
Med Phys; 2016 Jun; 43(6):2821-2827. PubMed ID: 27277030
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data.
Chmelik J; Jakubicek R; Walek P; Jan J; Ourednicek P; Lambert L; Amadori E; Gavelli G
Med Image Anal; 2018 Oct; 49():76-88. PubMed ID: 30114549
[TBL] [Abstract][Full Text] [Related]
20. Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients' CT scans?
Wang Z; van Dijk IWEM; Wiersma J; Ronckers CM; Oldenburger F; Balgobind BV; Bosman PAN; Bel A; Alderliesten T
Med Phys; 2018 Jun; 45(6):2628-2638. PubMed ID: 29637577
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]