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Introduction <p>Accurate segmentation of the lateral ventricles (LV) and choroid plexus (CP) in infant brain MRI is essential for understanding cerebrospinal fluid dynamics and early neurodevelopment. However, segmentation methods recently introduced for adult populations often underperform on infant data because of rapid anatomical changes, low tissue contrast, and motion artifacts, and they frequently misclassify tissue boundaries.</p> Method <p>To address these challenges, we propose a fully automated deep learning method for joint LV and CP segmentation using T1-weighted MRI (Baby Connectome Project (BCP) dataset total <i>n</i> = 154; in-house retrospective dataset <i>n</i> = 52). Our approach integrates an anatomy-aware loss function that explicitly enforces the topological constraint of CP containment within the LV. The method was validated on two independent datasets to demonstrate clinical adaptability using Dice score, 95% Hausdorff distance (HD95), and the average symmetric surface distance (ASSD).</p> Results <p>The method achieved Dice scores of 0.818 ± 0.075 for LV and 0.827 ± 0.084 for CP in the BCP dataset, with HD95 of 7.487 ± 7.351 mm and 4.925 ± 2.897 mm, and ASSD of 1.175 ± 0.524 mm and 0.818 ± 0.239 mm, respectively. In the in-house dataset, the method achieved Dice scores of 0.964 ± 0.060 for LV and 0.932 ± 0.059 for CP, with HD95 of 0.310 ± 0.542 mm and 4.148 ± 3.726 mm, and ASSD of 0.088 ± 0.151 mm and 0.280 ± 0.239 mm, respectively.</p> Conclusion <p>This method addresses limitations of prior methods by ensuring anatomical consistency without manual annotation. The approach has the potential to support large-scale studies investigating CP morphology and its relevance to early neurodevelopment.</p>

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Automatic lateral ventricle and choroid plexus segmentation method in infant brain MR images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月3日

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  • Junghwa Kang,
  • Hyun Gi Kim,
  • Na-Young Shin,
  • Yoonho Nam

摘要

Introduction

Accurate segmentation of the lateral ventricles (LV) and choroid plexus (CP) in infant brain MRI is essential for understanding cerebrospinal fluid dynamics and early neurodevelopment. However, segmentation methods recently introduced for adult populations often underperform on infant data because of rapid anatomical changes, low tissue contrast, and motion artifacts, and they frequently misclassify tissue boundaries.

Method

To address these challenges, we propose a fully automated deep learning method for joint LV and CP segmentation using T1-weighted MRI (Baby Connectome Project (BCP) dataset total n = 154; in-house retrospective dataset n = 52). Our approach integrates an anatomy-aware loss function that explicitly enforces the topological constraint of CP containment within the LV. The method was validated on two independent datasets to demonstrate clinical adaptability using Dice score, 95% Hausdorff distance (HD95), and the average symmetric surface distance (ASSD).

Results

The method achieved Dice scores of 0.818 ± 0.075 for LV and 0.827 ± 0.084 for CP in the BCP dataset, with HD95 of 7.487 ± 7.351 mm and 4.925 ± 2.897 mm, and ASSD of 1.175 ± 0.524 mm and 0.818 ± 0.239 mm, respectively. In the in-house dataset, the method achieved Dice scores of 0.964 ± 0.060 for LV and 0.932 ± 0.059 for CP, with HD95 of 0.310 ± 0.542 mm and 4.148 ± 3.726 mm, and ASSD of 0.088 ± 0.151 mm and 0.280 ± 0.239 mm, respectively.

Conclusion

This method addresses limitations of prior methods by ensuring anatomical consistency without manual annotation. The approach has the potential to support large-scale studies investigating CP morphology and its relevance to early neurodevelopment.