Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging MRI , computed tomography CT , and ultrasound and major anatomical structures of interest ventricles, atria, and vessels. In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches scarcity of labels, model generalizability across different domains, interpretability and suggest potential directions for future research. About The number is still increasing annually.
Vascular Image Registration Techniques: A Living Review
The convictions of the principal officers of the CPUSA were sustained—and the constitutionality of the advocacy provision of the Smith Act upheld—by the U. Supreme Court in Dennis v. United States In a later case, Yates v.
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