Cardiac Magnetic Resonance Image Segmentation with Anatomical Knowledge

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• Jinming Duan (School of Computer Science, University of Birmingham)
• Tuesday 14 July 2020, 14:00-15:00
• via Zoom, Meeting ID: 326 414 7316, Password: 772020..

If you have a question about this talk, please contact Hong Duong.

This talk focuses on segmentation of cardiac magnetic resonance (CMR) images from both healthy and pathological subjects. Specifically, we will propose three different approaches that explicitly consider geometry (anatomy) information of the heart.

First, we introduce a novel deep level set method, which explicitly considers the image features learned from a deep neural network. To this end, we estimate joint probability maps over both region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of pulmonary hypertension (PH) hearts, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. We show results on CMR cine images and demonstrate that the proposed method leads to substantial improvements for CMR image segmentation in PH patients.

Second, we propose a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline combines the computational advantage of 2.5D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. A refinement step is introduced for overcoming image artefacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. Extensive numerical experiments on the two large datasets show that our method is robust and capable of producing accurate, high-resolution, and anatomically smooth bi-ventricular 3D models, despite the presence of artefacts in input CMR volumes.

Lastly, accelerating the CMR acquisition is essential. However, reconstructing high-quality images from accelerated CMR acquisition is a nontrivial problem. As such, I will show how deep neural networks can be developed to bypass the usual image reconstruction stage. The method applies shape prior knowledge through an auto-encoder. Due to the prior knowledge, we improved both the CMR acquisition time and segmentation accuracy.

This talk is part of the Data Science and Computational Statistics Seminar series.

This talk is included in these lists:

• Data Science and Computational Statistics Seminar
• School of Mathematics events
• via Zoom, Meeting ID: 326 414 7316, Password: 772020.

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