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<p>Intracranial aneurysms are relatively common and life-threatening conditions, making precise segmentation during early diagnosis crucial. However, the challenges of poor imaging quality and high noise levels often result in unclear aneurysm edges. Additionally, the varying sizes of aneurysms further complicate accurate segmentation. To address these issues, we propose a <b>M</b>ultiscale and <b>E</b>dge-guided enhanced 3D deep learning model. First, the asymmetrically larger network with enhanced hierarchical feature representation effectively captures subtle image features, thereby improving the localization of anatomical structures. Second, the multi-scale feature fusion mechanism within the encoder improves feature diversity and edge information, enhancing segmentation precision for aneurysms of different sizes. Finally, the edge-guided attention technique within the decoder combines local features with predicted heatmaps to extract comprehensive edge information. The experimental results demonstrate that the model outperforms general models in five key metrics on the internal dataset. External dataset testing confirms its adaptability and robustness across data from different acquisition protocols and hardware configurations. Clinical trials have further validated its practicality, assisting radiologists in more accurate intracranial aneurysm diagnosis.</p>

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3D ME-Net: multi-scale and edge-guided enhancement network for intracranial aneurysm segmentation

  • 期刊论文

  • 发表日期：

    2025年8月22日

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  • Jiaqi Wang,
  • Juntong Liu,
  • Jun Li,
  • Aiping Wu,
  • Yunfeng Zhou,
  • Mingquan Ye

摘要

Intracranial aneurysms are relatively common and life-threatening conditions, making precise segmentation during early diagnosis crucial. However, the challenges of poor imaging quality and high noise levels often result in unclear aneurysm edges. Additionally, the varying sizes of aneurysms further complicate accurate segmentation. To address these issues, we propose a Multiscale and Edge-guided enhanced 3D deep learning model. First, the asymmetrically larger network with enhanced hierarchical feature representation effectively captures subtle image features, thereby improving the localization of anatomical structures. Second, the multi-scale feature fusion mechanism within the encoder improves feature diversity and edge information, enhancing segmentation precision for aneurysms of different sizes. Finally, the edge-guided attention technique within the decoder combines local features with predicted heatmaps to extract comprehensive edge information. The experimental results demonstrate that the model outperforms general models in five key metrics on the internal dataset. External dataset testing confirms its adaptability and robustness across data from different acquisition protocols and hardware configurations. Clinical trials have further validated its practicality, assisting radiologists in more accurate intracranial aneurysm diagnosis.