Key result
Current deep learning methods for CMR chamber segmentation face ongoing challenges and limitations.
Why the study?
Automatic cardiac MR segmentation faces challenges due to heart motion and image characteristics, motivating evaluation of deep learning methods for accurate heart chamber segmentation.
This review provides a comprehensive overview of deep learning techniques for cardiac MRI segmentation, highlighting current methods, datasets, and ongoing challenges.
Challenges in deep learning cardiac MR segmentation caution against routine clinical use; leaves open development of more robust, generalizable models.
This review provides an overview of cardiac segmentation by using Deep learning for MR images. Cardiac MR is widely used due to its unique capability as non invasive imaging. CMR images are used to derive cardiac indices to diagnose various cardiac diseases by segmentation of heart chambers. There are number of challenges in automatic cardiac MR segmentation due to characteristics of MR images because of continuous movement of heart. Now days, deep learning become widely used technique to segment heart chambers for efficient and accurate results. In this review, we provide overview of publically available CMRI datasets, descriptions of currently available methods to segment Left ventricle, Right ventricle, and Myocardium. After reviewing various techniques, we describe limitations and possible solutions. We conclude deep learning based segmentation issues in the currentapproaches.
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PARIKH et al. (2022) conducted a review in Cardiac diseases. Deep learning for cardiac MR segmentation was evaluated. Deep learning techniques for cardiac MR segmentation of the heart chambers face various challenges and limitations in current approaches.
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