论文标题
使用比例注意网络自动脑肿瘤分割
Automatic Brain Tumor Segmentation with Scale Attention Network
论文作者
论文摘要
自动分割脑肿瘤是提取定量成像生物标志物的必不可少但具有挑战性的步骤，以进行准确的肿瘤检测，诊断，预后，治疗计划和评估。多模式的脑肿瘤分割挑战2020（Brats 2020）提供了一个常见平台，用于比较1次在1个任务中的多参数磁共振成像（MPMRI）上的不同自动算法）MRI扫描； 2）通过术前MRI扫描预测患者总生存期（OS）； 3）与治疗相关效果的真实肿瘤复发的区别和4）评估分割中的不确定性度量。我们通过基于编码器架构建立一个全自动分割网络来参与图像分割挑战。为了更好地整合不同尺度的信息，我们提出了一种动态尺度注意机制，该机制将低级细节与来自不同尺度的特征地图的高级语义结合在一起。 Our framework was trained using the 369 challenge training cases provided by BraTS 2020, and achieved an average Dice Similarity Coefficient (DSC) of 0.8828, 0.8433 and 0.8177, as well as 95% Hausdorff distance (in millimeter) of 5.2176, 17.9697 and 13.4298 on 166 testing cases for whole tumor, tumor core and enhanced tumor, respectively, which在2020年挑战赛中，在693个注册中排名第三。
Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. Multimodal Brain Tumor Segmentation Challenge 2020 (BraTS 2020) provides a common platform for comparing different automatic algorithms on multi-parametric Magnetic Resonance Imaging (mpMRI) in tasks of 1) Brain tumor segmentation MRI scans; 2) Prediction of patient overall survival (OS) from pre-operative MRI scans; 3) Distinction of true tumor recurrence from treatment related effects and 4) Evaluation of uncertainty measures in segmentation. We participate the image segmentation challenge by developing a fully automatic segmentation network based on encoder-decoder architecture. In order to better integrate information across different scales, we propose a dynamic scale attention mechanism that incorporates low-level details with high-level semantics from feature maps at different scales. Our framework was trained using the 369 challenge training cases provided by BraTS 2020, and achieved an average Dice Similarity Coefficient (DSC) of 0.8828, 0.8433 and 0.8177, as well as 95% Hausdorff distance (in millimeter) of 5.2176, 17.9697 and 13.4298 on 166 testing cases for whole tumor, tumor core and enhanced tumor, respectively, which ranked itself as the 3rd place among 693 registrations in the BraTS 2020 challenge.
