标题： 基于多通道MRI的缺血性脑卒中病灶分割的深度学习驱动方法 显示英文标题

标题： Deep Learning-Driven Segmentation of Ischemic Stroke Lesions Using Multi-Channel MRI

摘要： 缺血性脑卒中是由脑血管阻塞引起的，由于脑卒中病变的多样性和微妙性，在医学影像中的诊断存在相当大的挑战。磁共振成像（MRI）在诊断和管理缺血性脑卒中中起着关键作用，但现有的分割技术往往无法准确界定病变。本研究介绍了一种基于深度学习的新方法，使用多通道MRI模态（包括弥散加权成像（DWI）、表观扩散系数（ADC）和增强弥散加权成像（eDWI））来分割缺血性脑卒中病变。所提出的架构将DenseNet121作为编码器，并在解码器中使用自组织操作神经网络（SelfONN），并通过通道和空间复合注意（CSCA）和双挤压-激励（DSE）模块进行了增强。此外，引入了一种自定义损失函数，结合了Dice损失和Jaccard损失，并采用加权平均值以提高模型性能。该模型在ISLES 2022数据集上训练和评估，达到了Dice相似性系数（DSC）为83.88到85.86。这种方法不仅优于现有方法，还解决了当前分割实践中的关键局限性。这些进展显著提高了缺血性脑卒中的诊断精度和治疗计划，为临床决策提供了宝贵支持。 显示英文摘要

摘要： Ischemic stroke, caused by cerebral vessel occlusion, presents substantial challenges in medical imaging due to the variability and subtlety of stroke lesions. Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing and managing ischemic stroke, yet existing segmentation techniques often fail to accurately delineate lesions. This study introduces a novel deep learning-based method for segmenting ischemic stroke lesions using multi-channel MRI modalities, including Diffusion Weighted Imaging (DWI), Apparent Diffusion Coefficient (ADC), and enhanced Diffusion Weighted Imaging (eDWI). The proposed architecture integrates DenseNet121 as the encoder with Self-Organized Operational Neural Networks (SelfONN) in the decoder, enhanced by Channel and Space Compound Attention (CSCA) and Double Squeeze-and-Excitation (DSE) blocks. Additionally, a custom loss function combining Dice Loss and Jaccard Loss with weighted averages is introduced to improve model performance. Trained and evaluated on the ISLES 2022 dataset, the model achieved Dice Similarity Coefficients (DSC) of 83.88% using DWI alone, 85.86% with DWI and ADC, and 87.49% with the integration of DWI, ADC, and eDWI. This approach not only outperforms existing methods but also addresses key limitations in current segmentation practices. These advancements significantly enhance diagnostic precision and treatment planning for ischemic stroke, providing valuable support for clinical decision-making.