Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors
CoRR(2024)
摘要
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.
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