210 related articles for article (PubMed ID: 34333477)
1. Fully automatic brain tumor segmentation for 3D evaluation in augmented reality.
Fick T; van Doormaal JAM; Tosic L; van Zoest RJ; Meulstee JW; Hoving EW; van Doormaal TPC
Neurosurg Focus; 2021 Aug; 51(2):E14. PubMed ID: 34333477
[TBL] [Abstract][Full Text] [Related]
2. Fully Automatic Adaptive Meshing Based Segmentation of the Ventricular System for Augmented Reality Visualization and Navigation.
van Doormaal JAM; Fick T; Ali M; Köllen M; van der Kuijp V; van Doormaal TPC
World Neurosurg; 2021 Dec; 156():e9-e24. PubMed ID: 34333157
[TBL] [Abstract][Full Text] [Related]
3. NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans.
de Boer M; Kos TM; Fick T; van Doormaal JAM; Colombo E; Kuijf HJ; Robe PAJT; Regli LP; Bartels LW; van Doormaal TPC
Acta Neurochir (Wien); 2024 Feb; 166(1):92. PubMed ID: 38376564
[TBL] [Abstract][Full Text] [Related]
4. Nextmed: Automatic Imaging Segmentation, 3D Reconstruction, and 3D Model Visualization Platform Using Augmented and Virtual Reality.
González Izard S; Sánchez Torres R; Alonso Plaza Ó; Juanes Méndez JA; García-Peñalvo FJ
Sensors (Basel); 2020 May; 20(10):. PubMed ID: 32456194
[TBL] [Abstract][Full Text] [Related]
5. Three-dimensional multipath DenseNet for improving automatic segmentation of glioblastoma on pre-operative multimodal MR images.
Fu J; Singhrao K; Qi XS; Yang Y; Ruan D; Lewis JH
Med Phys; 2021 Jun; 48(6):2859-2866. PubMed ID: 33621350
[TBL] [Abstract][Full Text] [Related]
6. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model.
Gan Y; Xia Z; Xiong J; Zhao Q; Hu Y; Zhang J
Med Phys; 2015 Jan; 42(1):14-27. PubMed ID: 25563244
[TBL] [Abstract][Full Text] [Related]
7. Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.
Perkuhn M; Stavrinou P; Thiele F; Shakirin G; Mohan M; Garmpis D; Kabbasch C; Borggrefe J
Invest Radiol; 2018 Nov; 53(11):647-654. PubMed ID: 29863600
[TBL] [Abstract][Full Text] [Related]
8. Fully automated segmentation of brain tumor from multiparametric MRI using 3D context deep supervised U-Net.
Lin M; Momin S; Lei Y; Wang H; Curran WJ; Liu T; Yang X
Med Phys; 2021 Aug; 48(8):4365-4374. PubMed ID: 34101845
[TBL] [Abstract][Full Text] [Related]
9. Multiscale Local Enhancement Deep Convolutional Networks for the Automated 3D Segmentation of Gross Tumor Volumes in Nasopharyngeal Carcinoma: A Multi-Institutional Dataset Study.
Yang G; Dai Z; Zhang Y; Zhu L; Tan J; Chen Z; Zhang B; Cai C; He Q; Li F; Wang X; Yang W
Front Oncol; 2022; 12():827991. PubMed ID: 35387126
[TBL] [Abstract][Full Text] [Related]
10. Spatially varying accuracy and reproducibility of prostate segmentation in magnetic resonance images using manual and semiautomated methods.
Shahedi M; Cool DW; Romagnoli C; Bauman GS; Bastian-Jordan M; Gibson E; Rodrigues G; Ahmad B; Lock M; Fenster A; Ward AD
Med Phys; 2014 Nov; 41(11):113503. PubMed ID: 25370674
[TBL] [Abstract][Full Text] [Related]
11. Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.
Orlando N; Gillies DJ; Gyacskov I; Romagnoli C; D'Souza D; Fenster A
Med Phys; 2020 Jun; 47(6):2413-2426. PubMed ID: 32166768
[TBL] [Abstract][Full Text] [Related]
12. Tissue segmentation of head and neck CT images for treatment planning: a multiatlas approach combined with intensity modeling.
Fortunati V; Verhaart RF; van der Lijn F; Niessen WJ; Veenland JF; Paulides MM; van Walsum T
Med Phys; 2013 Jul; 40(7):071905. PubMed ID: 23822442
[TBL] [Abstract][Full Text] [Related]
13. Automatic segmentation of vestibular schwannomas from T1-weighted MRI with a deep neural network.
Wang H; Qu T; Bernstein K; Barbee D; Kondziolka D
Radiat Oncol; 2023 May; 18(1):78. PubMed ID: 37158968
[TBL] [Abstract][Full Text] [Related]
14. Automatic segmentation of brain metastases using T1 magnetic resonance and computed tomography images.
Hsu DG; Ballangrud Å; Shamseddine A; Deasy JO; Veeraraghavan H; Cervino L; Beal K; Aristophanous M
Phys Med Biol; 2021 Aug; 66(17):. PubMed ID: 34315148
[TBL] [Abstract][Full Text] [Related]
15. Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy.
Zhu J; Bolsterlee B; Chow BVY; Cai C; Herbert RD; Song Y; Meijering E
NMR Biomed; 2021 Dec; 34(12):e4609. PubMed ID: 34545647
[TBL] [Abstract][Full Text] [Related]
16. An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.
Fashandi H; Kuling G; Lu Y; Wu H; Martel AL
Med Phys; 2019 Mar; 46(3):1230-1244. PubMed ID: 30609062
[TBL] [Abstract][Full Text] [Related]
17. An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI.
Shapey J; Wang G; Dorent R; Dimitriadis A; Li W; Paddick I; Kitchen N; Bisdas S; Saeed SR; Ourselin S; Bradford R; Vercauteren T
J Neurosurg; 2019 Dec; 134(1):171-179. PubMed ID: 31812137
[TBL] [Abstract][Full Text] [Related]
18. Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging.
Shahedi M; Cool DW; Bauman GS; Bastian-Jordan M; Fenster A; Ward AD
J Digit Imaging; 2017 Dec; 30(6):782-795. PubMed ID: 28342043
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. nnUnetFormer: an automatic method based on nnUnet and transformer for brain tumor segmentation with multimodal MR images.
Guo S; Chen Q; Wang L; Wang L; Zhu Y
Phys Med Biol; 2023 Dec; 68(24):. PubMed ID: 37963410
[No Abstract] [Full Text] [Related]
[Next] [New Search]