基于磁共振成像特征的亚型分类与模型集成用于增强脑肿瘤分割
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation
AI总结：
针对BraTS 2024挑战赛中多参数MRI脑肿瘤异质性分割难题，提出结合最先进模型的深度学习集成方法，并引入基于MRI影像组学的自适应预处理与后处理技术以区分肿瘤亚型，在PED、MEN-RT和MET测试集上取得了优异的分割性能与泛化能力。
摘要：
多参数磁共振成像（mpMRI）中脑肿瘤的准确自动分割对定量测量至关重要，这在临床诊断和预后中发挥着日益重要的作用。2024年国际脑肿瘤分割（BraTS）挑战赛提供了独特的基准测试机会，涵盖成人和儿童人群中的多种脑肿瘤，如儿童脑肿瘤（PED）、脑膜瘤（MEN-RT）和脑转移瘤（MET）等。与往届相比，BraTS 2024实施了多项变更以大幅提升临床相关性，例如细化了用于评估的肿瘤区域。我们提出了一种基于深度学习的集成方法，整合了最先进的分割模型。此外，我们引入了创新的自适应预处理和后处理技术，采用基于MRI的影像组学分析来区分肿瘤亚型。鉴于BraTS数据集中肿瘤具有异质性，该方法提升了分割模型的精度和泛化能力。在最终测试集上，我们的方法在PED、MEN-RT和MET的整体肿瘤中，分别实现了0.926、0.801和0.688的平均病灶级Dice相似系数。这些结果证明了该方法在提升各类脑肿瘤分割性能和泛化能力方面的有效性。我们实现的源代码可在 https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids 获取。此外，开源Web应用程序可在 https://segmenter.hope4kids.io/ 访问，该应用使用Docker容器 aparida12/brats-peds-2024:v20240913。
英文摘要：
Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly important role in clinical diagnosis and prognosis. The International Brain Tumor Segmentation (BraTS) Challenge 2024 offers a unique benchmarking opportunity, including various types of brain tumors in both adult and pediatric populations, such as pediatric brain tumors (PED), meningiomas (MEN-RT) and brain metastases (MET), among others. Compared to previous editions, BraTS 2024 has implemented changes to substantially increase clinical relevance, such as refined tumor regions for evaluation. We propose a deep learning-based ensemble approach that integrates state-of-the-art segmentation models. Additionally, we introduce innovative, adaptive pre- and post-processing techniques that employ MRI-based radiomic analyses to differentiate tumor subtypes. Given the heterogeneous nature of the tumors present in the BraTS datasets, this approach enhances the precision and generalizability of segmentation models. On the final testing sets, our method achieved mean lesion-wise Dice similarity coefficients of 0.926, 0.801, and 0.688 for the whole tumor in PED, MEN-RT, and MET, respectively. These results demonstrate the effectiveness of our approach in improving segmentation performance and generalizability for various brain tumor types. The source code of our implementation is available at https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids. Additionally, an open-source web-application is accessible at https://segmenter.hope4kids.io/ which uses the docker container aparida12/brats-peds-2024:v20240913 .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
