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Objective <p>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.</p> Materials and methods <p>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&#xa0;mm (a mean DSC of 0.885 ± 0.065 and a mean HD95 of 1.57 ± 1.35&#xa0;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&#xa0;mm.</p> Results <p>Statistical analysis reveals significant improvements (<i>p</i>-value &lt; 0.001) for these regions, with DSC increases ranging from 1.3 to 3.2% and HD95 reductions of 0.06–0.33&#xa0;mm. Comparisons with recent studies highlight the superior performance of the performed method.</p> Discussion <p>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.</p>

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A dual-stage framework for segmentation of the brain anatomical regions with high accuracy

  • 期刊论文

  • 发表日期：

    2025年3月5日

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  • Peyman Sharifian,
  • Alireza Karimian,
  • Hossein Arabi

摘要

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.