SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input patches, combined-connections, long skip connections,...

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Detalles Bibliográficos
Autores principales: Yamanakkanavar, Nagaraj, Choi, Jae Young, Lee, Bumshik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319649/
https://www.ncbi.nlm.nih.gov/pubmed/35890829
http://dx.doi.org/10.3390/s22145148

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319649/
https://www.ncbi.nlm.nih.gov/pubmed/35890829
http://dx.doi.org/10.3390/s22145148

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