Semantic Segmentation of Hippocampal Subregions With U-Net Architecture

Soraya Nasser, Moulkheir Naoui, Ghalem Belalem, Saïd Mahmoudi

Source Title: International Journal of E-Health and Medical Communications (IJEHMC)12(6)

ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799867517|DOI: 10.4018/IJEHMC.20211101.oa4

MLA

Nasser, Soraya, et al. "Semantic Segmentation of Hippocampal Subregions With U-Net Architecture." IJEHMC vol.12, no.6 2021: pp.1-20. http://doi.org/10.4018/IJEHMC.20211101.oa4

APA

Nasser, S., Naoui, M., Belalem, G., & Mahmoudi, S. (2021). Semantic Segmentation of Hippocampal Subregions With U-Net Architecture. International Journal of E-Health and Medical Communications (IJEHMC), 12(6), 1-20. http://doi.org/10.4018/IJEHMC.20211101.oa4

Chicago

Nasser, Soraya, et al. "Semantic Segmentation of Hippocampal Subregions With U-Net Architecture," International Journal of E-Health and Medical Communications (IJEHMC) 12, no.6: 1-20. http://doi.org/10.4018/IJEHMC.20211101.oa4

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Abstract

The Automatic semantic segmentation of the hippocampus is an important area of research in which several convolutional neural networks (CNN) models have been used to detect the hippocampus from whole cerebral MRI. In this paper we present two convolutional neural networks the first network ( Hippocampus Segmentation Single Entity HSSE) segmented the hippocampus as a single entity and the second used to detect the hippocampal sub-regions ( Hippocampus Segmentation Multi Class HSMC), these two networks inspire their architecture of the U-net model. Two cohorts were used as training data from (NITRC) (NeuroImaging Tools & Resources Collaboratory (NITRC)) annotated by ITK-SNAP software. We analyze this networks alongside other recent methods that do hippocampal segmentation, the results obtained are encouraging and reach dice scores greater than 0.84