Cascade Disentangled Quality Enhancement for Improved Biventricular Segmentation in Cardiac Cine MRI

Motivation: Achieving accurate segmentation is essential for cardiac MRI analysis. However, variability in image quality in clinical datasets can affect segmentation performance. Goal(s): To address this, we aim to utilize image enhancement to improve overall image quality, thus enhancing segmentation accuracy. This process, however, depends on well-defined, high-quality reference images to guide the enhancement process. Approach: In this study, we defined high-quality images across three domains and used them as targets in a 3-stage cascade enhancement for a cine MRI dataset, with each stage refining a specific quality domain. Results: This approach progressively improved the dataset quality, leading to better segmentation performance. Impact: The image-quality assessment strategy may be helpful in image quality evaluation. The proposed method probably could be extended to other imaging applications with various datasets, potentially improving the accuracy in clinical diagnosis and prediction.