Abstract
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.
Abstract (translated)
我们提出了一个新颖的多阶段多视角心肌图像分割架构。我们的方法利用长轴（2D）和短轴（3D）磁共振（MR）图像之间的关系进行级联3D-to-2D-to-3D分割，分割长轴和短轴图像。在第一阶段，使用短轴图像进行3D分割，并将预测转换为长轴视图，用作下一阶段的分割先决条件。在第二阶段，使用心定位和裁剪（HLC）模块将心区域定位和裁剪在分割先决条件周围，将后续模型聚焦于图像中的心区域，并进行2D分割。同样，我们将长轴预测转换为短轴视图，将心区域定位和裁剪，并再次进行3D分割，以优化初始的短轴分割。我们在M&M-2数据集上评估我们的方法，该数据集包括多病种、多视角和多中心右心室分割。我们的方法在短轴和长轴图像中分割感兴趣的心脏区域方面均优于最先进的Methods。预训练模型、源代码和实现细节将公开可用。
URL
https://arxiv.org/abs/2404.16708
