208 related articles for article (PubMed ID: 37696029)
21. Improving brain tumor segmentation on MRI based on the deep U-net and residual units.
Yang T; Song J; Li L; Tang Q
J Xray Sci Technol; 2020; 28(1):95-110. PubMed ID: 31839620
[TBL] [Abstract][Full Text] [Related]
22. Study of multistep Dense U-Net-based automatic segmentation for head MRI scans.
Gi Y; Oh G; Jo Y; Lim H; Ko Y; Hong J; Lee E; Park S; Kwak T; Kim S; Yoon M
Med Phys; 2024 Mar; 51(3):2230-2238. PubMed ID: 37956307
[TBL] [Abstract][Full Text] [Related]
23. 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]
24. Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.
Lee B; Yamanakkanavar N; Choi JY
PLoS One; 2020; 15(8):e0236493. PubMed ID: 32745102
[TBL] [Abstract][Full Text] [Related]
25. RSU-Net: U-net based on residual and self-attention mechanism in the segmentation of cardiac magnetic resonance images.
Li YZ; Wang Y; Huang YH; Xiang P; Liu WX; Lai QQ; Gao YY; Xu MS; Guo YF
Comput Methods Programs Biomed; 2023 Apr; 231():107437. PubMed ID: 36863157
[TBL] [Abstract][Full Text] [Related]
26. Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study.
Kazerooni AF; Arif S; Madhogarhia R; Khalili N; Haldar D; Bagheri S; Familiar AM; Anderson H; Haldar S; Tu W; Kim MC; Viswanathan K; Muller S; Prados M; Kline C; Vidal L; Aboian M; Storm PB; Resnick AC; Ware JB; Vossough A; Davatzikos C; Nabavizadeh A
medRxiv; 2023 Jan; ():. PubMed ID: 36711966
[TBL] [Abstract][Full Text] [Related]
27. Automated segmentation of the human supraclavicular fat depot via deep neural network in water-fat separated magnetic resonance images.
Zhao Y; Tang C; Cui B; Somasundaram A; Raspe J; Hu X; Holzapfel C; Junker D; Hauner H; Menze B; Wu M; Karampinos D
Quant Imaging Med Surg; 2023 Jul; 13(7):4699-4715. PubMed ID: 37456284
[TBL] [Abstract][Full Text] [Related]
28. Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.
Zhang Y; Chan S; Chen JH; Chang KT; Lin CY; Pan HB; Lin WC; Kwong T; Parajuli R; Mehta RS; Chien SH; Su MY
J Digit Imaging; 2021 Aug; 34(4):877-887. PubMed ID: 34244879
[TBL] [Abstract][Full Text] [Related]
29. Explainable AI for CNN-based prostate tumor segmentation in multi-parametric MRI correlated to whole mount histopathology.
Gunashekar DD; Bielak L; Hägele L; Oerther B; Benndorf M; Grosu AL; Brox T; Zamboglou C; Bock M
Radiat Oncol; 2022 Apr; 17(1):65. PubMed ID: 35366918
[TBL] [Abstract][Full Text] [Related]
30. A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture.
Shaukat Z; Farooq QUA; Tu S; Xiao C; Ali S
BMC Bioinformatics; 2022 Jun; 23(1):251. PubMed ID: 35751030
[TBL] [Abstract][Full Text] [Related]
31. A data augmentation method for fully automatic brain tumor segmentation.
Wang Y; Ji Y; Xiao H
Comput Biol Med; 2022 Oct; 149():106039. PubMed ID: 36055163
[TBL] [Abstract][Full Text] [Related]
32. Region-related focal loss for 3D brain tumor MRI segmentation.
Li B; You X; Peng Q; Wang J; Yang C
Med Phys; 2023 Jul; 50(7):4325-4339. PubMed ID: 36708251
[TBL] [Abstract][Full Text] [Related]
33. Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images.
Naser MA; Deen MJ
Comput Biol Med; 2020 Jun; 121():103758. PubMed ID: 32568668
[TBL] [Abstract][Full Text] [Related]
34. Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation.
Ottom MA; Rahman HA; Dinov ID
IEEE J Transl Eng Health Med; 2022; 10():1800508. PubMed ID: 35774412
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. mResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI.
Li P; Li Z; Wang Z; Li C; Wang M
Med Biol Eng Comput; 2024 Mar; 62(3):641-651. PubMed ID: 37981627
[TBL] [Abstract][Full Text] [Related]
37. SDResU-Net: Separable and Dilated Residual U-Net for MRI Brain Tumor Segmentation.
Zhang J; Lv X; Sun Q; Zhang Q; Wei X; Liu B
Curr Med Imaging; 2020; 16(6):720-728. PubMed ID: 32723244
[TBL] [Abstract][Full Text] [Related]
38. An Efficient Optimization Approach for Glioma Tumor Segmentation in Brain MRI.
Barzegar Z; Jamzad M
J Digit Imaging; 2022 Dec; 35(6):1634-1647. PubMed ID: 35995900
[TBL] [Abstract][Full Text] [Related]
39. Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study.
Kim DH; Seo J; Lee JH; Jeon ET; Jeong D; Chae HD; Lee E; Kang JH; Choi YH; Kim HJ; Chai JW
Korean J Radiol; 2024 Apr; 25(4):363-373. PubMed ID: 38528694
[TBL] [Abstract][Full Text] [Related]
40. Comparison of Prostate MRI Lesion Segmentation Agreement Between Multiple Radiologists and a Fully Automatic Deep Learning System.
Schelb P; Tavakoli AA; Tubtawee T; Hielscher T; Radtke JP; Görtz M; Schütz V; Kuder TA; Schimmöller L; Stenzinger A; Hohenfellner M; Schlemmer HP; Bonekamp D
Rofo; 2021 May; 193(5):559-573. PubMed ID: 33212541
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]