Open AccessSEMANTIC SEGMENTATION OF HIPPOCAMPAL SUBREGIONS WITH U-NET ARCHITECTURE
Publication year - 2021
Publication title -
international journal of e-health and medical communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 12
eISSN - 1947-3168
pISSN - 1947-315X
DOI - 10.4018/ijehmc.20211101oa08
Subject(s) - segmentation , computer science , convolutional neural network , hippocampus , hippocampal formation , artificial intelligence , neuroimaging , pattern recognition (psychology) , neuroscience , psychology
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