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Exclusive Independent Probability Estimation using Deep 3D Fully Convolutional DenseNets: Application to IsoIntense Infant Brain MRI Segmentation [article]

Seyed Raein Hashemi, Sanjay P. Prabhu, Simon K. Warfield, Ali Gholipour
2018 arXiv   pre-print
the isointense infant brain MRI segmentation (iSeg) challenge according to the official challenge test results.  ...  The most recent fast and accurate image segmentation methods are built upon fully convolutional deep neural networks.  ...  Experimental design We trained and tested our 3D FC-DenseNet with F β loss layer to segment isointense infant brains.  ... 
arXiv:1809.08168v3 fatcat:4tqhh62kb5e3jcs5ilw3oobmoe

Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI

Jinyoung Kim, Remi Patriat, Jordan Kaplan, Oren Solomon, Noam Harel
2020 IEEE Access  
It is thus a crucial step to accurately segment deep cerebellar nuclei for the understanding of the cerebellum system and its utility in deep brain stimulation treatment.  ...  We validate the proposed framework using 7T B0 MRIs from 60 subjects.  ...  [36] proposes an exclusive multi-label multi-class training strategy for infant brain tissue segmentation. Also, Milletari et al.  ... 
doi:10.1109/access.2020.2998537 pmid:32656051 pmcid:PMC7351101 fatcat:ihkyozka7vbhvk7gxa4qkohxqy

Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI [article]

Jinyoung Kim, Remi Patriat, Jordan Kaplan, Oren Solomon, Noam Harel
2020 arXiv   pre-print
It is thus a crucial step to accurately segment deep cerebellar nuclei for the understanding of the cerebellum system and its utility in deep brain stimulation treatment.  ...  We validate the proposed framework using 7T B0 MRIs from 60 subjects.  ...  [36] proposes an exclusive multi-label multi-class training strategy for infant brain tissue segmentation. Also, Milletari el al.  ... 
arXiv:2004.09788v3 fatcat:io34ocviqnfq7eeudkjxdyefgy