Multi-view Cardiac Image Segmentation via Trans-Dimensional Priors

TL;DR

This paper introduces a multi-stage trans-dimensional approach for cardiac MRI segmentation that leverages relationships between 2D and 3D views to improve accuracy in segmenting heart regions.

Contribution

It presents a novel sequential 3D-to-2D-to-3D segmentation architecture exploiting view relationships and a heart localization module, outperforming existing methods.

Findings

Outperforms state-of-the-art in cardiac segmentation

Effective use of view transformation and localization

Improved segmentation accuracy on M&Ms-2 dataset

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.