596 related articles for article (PubMed ID: 32060575)
21. Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan.
Eisma JJ; McKnight CD; Hett K; Elenberger J; Han CJ; Song AK; Considine C; Claassen DO; Donahue MJ
Fluids Barriers CNS; 2024 Feb; 21(1):21. PubMed ID: 38424598
[TBL] [Abstract][Full Text] [Related]
22. A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.
Ushinsky A; Bardis M; Glavis-Bloom J; Uchio E; Chantaduly C; Nguyentat M; Chow D; Chang PD; Houshyar R
AJR Am J Roentgenol; 2021 Jan; 216(1):111-116. PubMed ID: 32812797
[TBL] [Abstract][Full Text] [Related]
23. Deep Learning-Based Automated Abdominal Organ Segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies.
Kart T; Fischer M; Küstner T; Hepp T; Bamberg F; Winzeck S; Glocker B; Rueckert D; Gatidis S
Invest Radiol; 2021 Jun; 56(6):401-408. PubMed ID: 33930003
[TBL] [Abstract][Full Text] [Related]
24. Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.
Di Ieva A; Russo C; Liu S; Jian A; Bai MY; Qian Y; Magnussen JS
Neuroradiology; 2021 Aug; 63(8):1253-1262. PubMed ID: 33501512
[TBL] [Abstract][Full Text] [Related]
25. Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.
Ma X; Zhao L; Dang S; Zhao Y; Lu Y; Li X; Li P; Chen Y; Mei N; Yin B; Geng D
J Comput Assist Tomogr; 2024 May-Jun 01; 48(3):480-490. PubMed ID: 38013244
[TBL] [Abstract][Full Text] [Related]
26. A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma.
Maki S; Furuya T; Horikoshi T; Yokota H; Mori Y; Ota J; Kawasaki Y; Miyamoto T; Norimoto M; Okimatsu S; Shiga Y; Inage K; Orita S; Takahashi H; Suyari H; Uno T; Ohtori S
Spine (Phila Pa 1976); 2020 May; 45(10):694-700. PubMed ID: 31809468
[TBL] [Abstract][Full Text] [Related]
27. A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.
LaBella D; Khanna O; McBurney-Lin S; Mclean R; Nedelec P; Rashid AS; Tahon NH; Altes T; Baid U; Bhalerao R; Dhemesh Y; Floyd S; Godfrey D; Hilal F; Janas A; Kazerooni A; Kent C; Kirkpatrick J; Kofler F; Leu K; Maleki N; Menze B; Pajot M; Reitman ZJ; Rudie JD; Saluja R; Velichko Y; Wang C; Warman PI; Sollmann N; Diffley D; Nandolia KK; Warren DI; Hussain A; Fehringer JP; Bronstein Y; Deptula L; Stein EG; Taherzadeh M; Portela de Oliveira E; Haughey A; Kontzialis M; Saba L; Turner B; Brüßeler MMT; Ansari S; Gkampenis A; Weiss DM; Mansour A; Shawali IH; Yordanov N; Stein JM; Hourani R; Moshebah MY; Abouelatta AM; Rizvi T; Willms K; Martin DC; Okar A; D'Anna G; Taha A; Sharifi Y; Faghani S; Kite D; Pinho M; Haider MA; Alonso-Basanta M; Villanueva-Meyer J; Rauschecker AM; Nada A; Aboian M; Flanders A; Bakas S; Calabrese E
Sci Data; 2024 May; 11(1):496. PubMed ID: 38750041
[TBL] [Abstract][Full Text] [Related]
28. A dual-branch hybrid dilated CNN model for the AI-assisted segmentation of meningiomas in MR images.
Ma X; Zhao Y; Lu Y; Li P; Li X; Mei N; Wang J; Geng D; Zhao L; Yin B
Comput Biol Med; 2022 Dec; 151(Pt A):106279. PubMed ID: 36375416
[TBL] [Abstract][Full Text] [Related]
29. Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI.
Grøvik E; Yi D; Iv M; Tong E; Rubin D; Zaharchuk G
J Magn Reson Imaging; 2020 Jan; 51(1):175-182. PubMed ID: 31050074
[TBL] [Abstract][Full Text] [Related]
30. IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.
Choi Y; Nam Y; Lee YS; Kim J; Ahn KJ; Jang J; Shin NY; Kim BS; Jeon SS
Eur J Radiol; 2020 Jul; 128():109031. PubMed ID: 32417712
[TBL] [Abstract][Full Text] [Related]
31. Deep Learning Network for Segmentation of the Prostate Gland With Median Lobe Enlargement in T2-weighted MR Images: Comparison With Manual Segmentation Method.
Salvaggio G; Comelli A; Portoghese M; Cutaia G; Cannella R; Vernuccio F; Stefano A; Dispensa N; La Tona G; Salvaggio L; Calamia M; Gagliardo C; Lagalla R; Midiri M
Curr Probl Diagn Radiol; 2022; 51(3):328-333. PubMed ID: 34315623
[TBL] [Abstract][Full Text] [Related]
32. MRI-based machine learning models predict the malignant biological behavior of meningioma.
Li M; Liu L; Qi J; Qiao Y; Zeng H; Jiang W; Zhu R; Chen F; Huang H; Wu S
BMC Med Imaging; 2023 Sep; 23(1):141. PubMed ID: 37759192
[TBL] [Abstract][Full Text] [Related]
33. Automatic MRI-based Three-dimensional Models of Hip Cartilage Provide Improved Morphologic and Biochemical Analysis.
Schmaranzer F; Helfenstein R; Zeng G; Lerch TD; Novais EN; Wylie JD; Kim YJ; Siebenrock KA; Tannast M; Zheng G
Clin Orthop Relat Res; 2019 May; 477(5):1036-1052. PubMed ID: 30998632
[TBL] [Abstract][Full Text] [Related]
34. Computer-based radiological longitudinal evaluation of meningiomas following stereotactic radiosurgery.
Shimol EB; Joskowicz L; Eliahou R; Shoshan Y
Int J Comput Assist Radiol Surg; 2018 Feb; 13(2):215-228. PubMed ID: 29032421
[TBL] [Abstract][Full Text] [Related]
35. Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks.
Novosad P; Fonov V; Collins DL;
Hum Brain Mapp; 2020 Feb; 41(2):309-327. PubMed ID: 31633863
[TBL] [Abstract][Full Text] [Related]
36. Automated volumetry of meningiomas in contrast-enhanced T1-Weighted MRI using deep learning.
Iwata T; Hirayama R; Yamada S; Kijima N; Okita Y; Kagawa N; Kishima H
World Neurosurg X; 2024 Apr; 22():100353. PubMed ID: 38455247
[TBL] [Abstract][Full Text] [Related]
37. A dual autoencoder and singular value decomposition based feature optimization for the segmentation of brain tumor from MRI images.
Aswani K; Menaka D
BMC Med Imaging; 2021 May; 21(1):82. PubMed ID: 33985449
[TBL] [Abstract][Full Text] [Related]
38. Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR.
Trebeschi S; van Griethuysen JJM; Lambregts DMJ; Lahaye MJ; Parmar C; Bakers FCH; Peters NHGM; Beets-Tan RGH; Aerts HJWL
Sci Rep; 2017 Jul; 7(1):5301. PubMed ID: 28706185
[TBL] [Abstract][Full Text] [Related]
39. Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI.
Ke C; Chen H; Lv X; Li H; Zhang Y; Chen M; Hu D; Ruan G; Zhang Y; Zhang Y; Liu L; Feng Y
J Magn Reson Imaging; 2020 Jun; 51(6):1810-1820. PubMed ID: 31710413
[TBL] [Abstract][Full Text] [Related]
40. Comparing lesion segmentation methods in multiple sclerosis: Input from one manually delineated subject is sufficient for accurate lesion segmentation.
Weeda MM; Brouwer I; de Vos ML; de Vries MS; Barkhof F; Pouwels PJW; Vrenken H
Neuroimage Clin; 2019; 24():102074. PubMed ID: 31734527
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
