We propose a novel multi-stage trans-dimensional architecture for multi-view
cardiac image segmentation. Our method exploits the relationship between
long-axis (2D) and short-axis (3D) magnetic resonance (MR) images to perform a
sequential 3D-to-2D-to-3D segmentation, segmenting the long-axis and short-axis
images. In the first stage, 3D segmentation is performed using the short-axis
image, and the prediction is transformed to the long-axis view and used as a
segmentation prior in the next stage. In the second step, the heart region is
localized and cropped around the segmentation prior using a Heart Localization
and Cropping (HLC) module, focusing the subsequent model on the heart region of
the image, where a 2D segmentation is performed. Similarly, we transform the
long-axis prediction to the short-axis view, localize and crop the heart region
and again perform a 3D segmentation to refine the initial short-axis
segmentation. We evaluate our proposed method on the Multi-Disease, Multi-View
& Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) dataset,
where our method outperforms state-of-the-art methods in segmenting cardiac
regions of interest in both short-axis and long-axis images. The pre-trained
models, source code, and implementation details will be publicly available.

我们提出了一种新的多阶段跨维度架构，用于多视角心脏图像分割。我们的方法利用短轴和长轴核磁共振（MR）图像之间的关系进行序列化的三维至二维至三维分割，分割长轴和短轴图像。我们的方法在分割短轴和长轴图像中的心脏感兴趣区域方面优于现有方法，在 Multi-Disease、Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) 数据集上进行了评估。