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A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures
Published on: July 2, 2014
A dual-stage framework for segmentation of the brain anatomical regions with high accuracy
Peyman Sharifian1, Alireza Karimian2, Hossein Arabi3
1Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Objective:
This study presents a novel deep learning-based framework for precise brain MR region segmentation, aiming to identify the location and the shape details of different anatomical structures within the brain.
Materials And Methods:
The approach uses a two-stage 3D segmentation technique on a dataset of adult subjects, including cognitively normal participants and individuals with cognitive decline. Stage 1 employs a 3D U-Net to segment 13 brain regions, achieving a mean DSC of 0.904 ± 0.060 and a mean HD95 of 1.52 ± 1.53 mm (a mean DSC of 0.885 ± 0.065 and a mean HD95 of 1.57 ± 1.35 mm for smaller parts). For challenging regions like hippocampus, thalamus, cerebrospinal fluid, amygdala, basal ganglia, and corpus callosum, Stage 2 with SegResNet refines segmentation, improving mean DSC to 0.921 ± 0.048 and HD95 to 1.17 ± 0.69 mm.
Results:
Statistical analysis reveals significant improvements (p-value < 0.001) for these regions, with DSC increases ranging from 1.3 to 3.2% and HD95 reductions of 0.06-0.33 mm. Comparisons with recent studies highlight the superior performance of the performed method.
Discussion:
The inclusion of a second stage for refining the segmentation of smaller regions demonstrates substantial improvements, establishing the framework's potential for precise and reliable brain region segmentation across diverse cognitive groups.
