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A crucial first step in the early detection and tracking of neurodevelopmental problems is the accurate segmentation of infant brain magnetic resonance imaging (MRI). However, the effectiveness of traditional segmentation techniques is limited by challenges such low tissue contrast, high anatomical variability, and tiny tissue volumes, especially in cerebrospinal fluid (CSF) and white matter (WM). To improve segmentation accuracy and focus on relevant anatomical characteristics, a convolutional neural network-based method in this paper that incorporates attention processes within a U-Net framework is proposed. Dice Similarity Coefficient (DSC), Modified Hausdorff Distance (MHD), and Average Surface Distance (ASD) were used to quantitatively assess WM, gray matter (GM), and CSF. With decreased MHD and ASD values across all tissue types, the proposed model surpassed a few cutting-edge techniques in boundary correctness and obtained competitive DSC scores (0.84 for WM, 0.92 for GM, and 0.86 for CSF). These findings show that the proposed approach offers a favourable trade-off between boundary localization and volumetric accuracy. According to the results, our method has a great deal of promise for use in clinical settings where accurate brain tissue segmentation is necessary for early infancy diagnosis and quantitative analysis.

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Deep Learning-Based Infant Brain Tissue Segmentation Using Attention-Gated U-Net

  • 会议论文

  • 发表日期：

    2026年

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  • Sandile Thamie Mhlanga,
  • Serestina Viriri

摘要

A crucial first step in the early detection and tracking of neurodevelopmental problems is the accurate segmentation of infant brain magnetic resonance imaging (MRI). However, the effectiveness of traditional segmentation techniques is limited by challenges such low tissue contrast, high anatomical variability, and tiny tissue volumes, especially in cerebrospinal fluid (CSF) and white matter (WM). To improve segmentation accuracy and focus on relevant anatomical characteristics, a convolutional neural network-based method in this paper that incorporates attention processes within a U-Net framework is proposed. Dice Similarity Coefficient (DSC), Modified Hausdorff Distance (MHD), and Average Surface Distance (ASD) were used to quantitatively assess WM, gray matter (GM), and CSF. With decreased MHD and ASD values across all tissue types, the proposed model surpassed a few cutting-edge techniques in boundary correctness and obtained competitive DSC scores (0.84 for WM, 0.92 for GM, and 0.86 for CSF). These findings show that the proposed approach offers a favourable trade-off between boundary localization and volumetric accuracy. According to the results, our method has a great deal of promise for use in clinical settings where accurate brain tissue segmentation is necessary for early infancy diagnosis and quantitative analysis.