论文标题
蛋白质：空间变压器特征心脏MRI分割的金字塔网络
Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation
论文作者
论文摘要
评估右心室（RV）的结构和功能对于诊断几种心脏病理很重要。但是，分割RV仍然比左心室（LV）更具挑战性。在本文中，我们专注于在短（SA）和长轴（LA）心脏MR图像中分割RV。对于此任务，我们提出了一个新的多输入/输出体系结构，即混合2D/3D几何空间变压器多通式特征金字塔（蛋糕）。我们的功能金字塔不仅允许多尺度功能输出，还允许多尺度SA和LA输入图像来扩展当前设计。通过层的重量共享，蛋彩将SA和LA图像之间学习的功能，并结合了几何目标变压器，以将预测的SA分割映射到LA空间。我们的模型分别为SA和LA的平均骰子得分为0.836和0.798，Hausdorff距离为26.31 mm和31.19毫米。这打开了将RV分割模型纳入临床工作流程的潜力。
Assessing the structure and function of the right ventricle (RV) is important in the diagnosis of several cardiac pathologies. However, it remains more challenging to segment the RV than the left ventricle (LV). In this paper, we focus on segmenting the RV in both short (SA) and long-axis (LA) cardiac MR images simultaneously. For this task, we propose a new multi-input/output architecture, hybrid 2D/3D geometric spatial TransformEr Multi-Pass fEature pyRAmid (Tempera). Our feature pyramid extends current designs by allowing not only a multi-scale feature output but multi-scale SA and LA input images as well. Tempera transfers learned features between SA and LA images via layer weight sharing and incorporates a geometric target transformer to map the predicted SA segmentation to LA space. Our model achieves an average Dice score of 0.836 and 0.798 for the SA and LA, respectively, and 26.31 mm and 31.19 mm Hausdorff distances. This opens up the potential for the incorporation of RV segmentation models into clinical workflows.
