Scholarlink

<p>In recent years, numerous algorithms have emerged for the segmentation of brain tumors, driven by advancements in deep learning techniques, where the objective is to identify and delineate various tumor sub-regions. While deep learning models like nnUNet have shown promising results in glioma segmentation, their effectiveness in segmenting other brain tumor subtypes, such as meningiomas and metastases, remains uncertain, especially when the available dataset lacks representative examples. To address this challenge, we propose a meta-transfer learning approach, which involves fine-tuning the nnUNet model on datasets containing meningiomas and metastases while leveraging the knowledge acquired from glioma segmentation. This approach aims to enhance the adaptability of nnUNet, allowing it to generalize better to diverse brain tumor types and potentially improving the accuracy of diagnosis and treatment planning for patients with meningiomas and metastases. Our proposed method significantly improves segmentation performance, achieving Dice coefficients of 0.8621 ± 0.2413 for Whole Tumor (WT) in meningiomas and 0.8141 ± 0.0562 for WT in metastases. These results set a new benchmark in brain tumor segmentation and pave the way for more robust and generalizable medical image analysis tools.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Meta transfer learning for brain tumor segmentation using nnUNet in meningioma and metastasis cases

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月28日

查看全文
  • Mehnaz Tabassum,
  • Antonio Di Ieva,
  • Sidong Liu

摘要

In recent years, numerous algorithms have emerged for the segmentation of brain tumors, driven by advancements in deep learning techniques, where the objective is to identify and delineate various tumor sub-regions. While deep learning models like nnUNet have shown promising results in glioma segmentation, their effectiveness in segmenting other brain tumor subtypes, such as meningiomas and metastases, remains uncertain, especially when the available dataset lacks representative examples. To address this challenge, we propose a meta-transfer learning approach, which involves fine-tuning the nnUNet model on datasets containing meningiomas and metastases while leveraging the knowledge acquired from glioma segmentation. This approach aims to enhance the adaptability of nnUNet, allowing it to generalize better to diverse brain tumor types and potentially improving the accuracy of diagnosis and treatment planning for patients with meningiomas and metastases. Our proposed method significantly improves segmentation performance, achieving Dice coefficients of 0.8621 ± 0.2413 for Whole Tumor (WT) in meningiomas and 0.8141 ± 0.0562 for WT in metastases. These results set a new benchmark in brain tumor segmentation and pave the way for more robust and generalizable medical image analysis tools.