These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
149 related articles for article (PubMed ID: 34666289)
1. ALL-Net: Anatomical information lesion-wise loss function integrated into neural network for multiple sclerosis lesion segmentation. Zhang H; Zhang J; Li C; Sweeney EM; Spincemaille P; Nguyen TD; Gauthier SA; Wang Y; Marcille M Neuroimage Clin; 2021; 32():102854. PubMed ID: 34666289 [TBL] [Abstract][Full Text] [Related]
2. A dense residual U-net for multiple sclerosis lesions segmentation from multi-sequence 3D MR images. Sarica B; Seker DZ; Bayram B Int J Med Inform; 2023 Feb; 170():104965. PubMed ID: 36580821 [TBL] [Abstract][Full Text] [Related]
4. Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. Aslani S; Dayan M; Storelli L; Filippi M; Murino V; Rocca MA; Sona D Neuroimage; 2019 Aug; 196():1-15. PubMed ID: 30953833 [TBL] [Abstract][Full Text] [Related]
5. Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss. Xu Y; Klyuzhin I; Harsini S; Ortiz A; Zhang S; Bénard F; Dodhia R; Uribe CF; Rahmim A; Lavista Ferres J Comput Biol Med; 2023 May; 158():106882. PubMed ID: 37037147 [TBL] [Abstract][Full Text] [Related]
6. Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning. Narayana PA; Coronado I; Sujit SJ; Sun X; Wolinsky JS; Gabr RE Magn Reson Imaging; 2020 Jan; 65():8-14. PubMed ID: 31670238 [TBL] [Abstract][Full Text] [Related]
7. Investigation of an efficient multi-modal convolutional neural network for multiple sclerosis lesion detection. Raab F; Malloni W; Wein S; Greenlee MW; Lang EW Sci Rep; 2023 Nov; 13(1):21154. PubMed ID: 38036638 [TBL] [Abstract][Full Text] [Related]
8. Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks. Krüger J; Opfer R; Gessert N; Ostwaldt AC; Manogaran P; Kitzler HH; Schlaefer A; Schippling S Neuroimage Clin; 2020; 28():102445. PubMed ID: 33038667 [TBL] [Abstract][Full Text] [Related]
9. Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation. Essa E; Aldesouky D; Hussein SE; Rashad MZ Med Biol Eng Comput; 2020 Sep; 58(9):2161-2175. PubMed ID: 32681214 [TBL] [Abstract][Full Text] [Related]
10. Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection. Hashemi SR; Salehi SSM; Erdogmus D; Prabhu SP; Warfield SK; Gholipour A IEEE Access; 2019; 7():721-1735. PubMed ID: 31528523 [TBL] [Abstract][Full Text] [Related]
11. LST-AI: A deep learning ensemble for accurate MS lesion segmentation. Wiltgen T; McGinnis J; Schlaeger S; Kofler F; Voon C; Berthele A; Bischl D; Grundl L; Will N; Metz M; Schinz D; Sepp D; Prucker P; Schmitz-Koep B; Zimmer C; Menze B; Rueckert D; Hemmer B; Kirschke J; Mühlau M; Wiestler B Neuroimage Clin; 2024; 42():103611. PubMed ID: 38703470 [TBL] [Abstract][Full Text] [Related]
12. QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps. Zhang H; Nguyen TD; Zhang J; Marcille M; Spincemaille P; Wang Y; Gauthier SA; Sweeney EM Neuroimage Clin; 2022; 34():102979. PubMed ID: 35247730 [TBL] [Abstract][Full Text] [Related]
13. Multiple Sclerosis Lesion Segmentation in Brain MRI Using Inception Modules Embedded in a Convolutional Neural Network. Ansari SU; Javed K; Qaisar SM; Jillani R; Haider U J Healthc Eng; 2021; 2021():4138137. PubMed ID: 34484652 [TBL] [Abstract][Full Text] [Related]
14. Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation. Nair T; Precup D; Arnold DL; Arbel T Med Image Anal; 2020 Jan; 59():101557. PubMed ID: 31677438 [TBL] [Abstract][Full Text] [Related]
15. Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis. Brugnara G; Isensee F; Neuberger U; Bonekamp D; Petersen J; Diem R; Wildemann B; Heiland S; Wick W; Bendszus M; Maier-Hein K; Kickingereder P Eur Radiol; 2020 Apr; 30(4):2356-2364. PubMed ID: 31900702 [TBL] [Abstract][Full Text] [Related]
16. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. Valverde S; Cabezas M; Roura E; González-Villà S; Pareto D; Vilanova JC; Ramió-Torrentà L; Rovira À; Oliver A; Lladó X Neuroimage; 2017 Jul; 155():159-168. PubMed ID: 28435096 [TBL] [Abstract][Full Text] [Related]
17. Automated Cervical Spinal Cord Segmentation in Real-World MRI of Multiple Sclerosis Patients by Optimized Hybrid Residual Attention-Aware Convolutional Neural Networks. Bueno A; Bosch I; Rodríguez A; Jiménez A; Carreres J; Fernández M; Marti-Bonmati L; Alberich-Bayarri A J Digit Imaging; 2022 Oct; 35(5):1131-1142. PubMed ID: 35789447 [TBL] [Abstract][Full Text] [Related]
18. Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images. Rachmadi MF; Valdés-Hernández MDC; Li H; Guerrero R; Meijboom R; Wiseman S; Waldman A; Zhang J; Rueckert D; Wardlaw J; Komura T Comput Med Imaging Graph; 2020 Jan; 79():101685. PubMed ID: 31846826 [TBL] [Abstract][Full Text] [Related]
19. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. Brosch T; Tang LY; Youngjin Yoo ; Li DK; Traboulsee A; Tam R IEEE Trans Med Imaging; 2016 May; 35(5):1229-1239. PubMed ID: 26886978 [TBL] [Abstract][Full Text] [Related]
20. Delve into Multiple Sclerosis (MS) lesion exploration: A modified attention U-Net for MS lesion segmentation in Brain MRI. Hashemi M; Akhbari M; Jutten C Comput Biol Med; 2022 Jun; 145():105402. PubMed ID: 35344864 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]