Artificial Intelligence in Computer Vision: Cardiac MRI and Multimodality Imaging Segmentation View Full Text


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Article Info

DATE

2021-08-04

AUTHORS

Alan C. Kwan, Gerran Salto, Susan Cheng, David Ouyang

ABSTRACT

Purpose of ReviewAnatomical segmentation has played a major role within clinical cardiology. Novel techniques through artificial intelligence-based computer vision have revolutionized this process through both automation and novel applications. This review discusses the history and clinical context of cardiac segmentation to provide a framework for a survey of recent manuscripts in artificial intelligence and cardiac segmentation. We aim to clarify for the reader the clinical question of “Why do we segment?” in order to understand the question of “Where is current research and where should be?”Recent FindingsThere has been increasing research in cardiac segmentation in recent years. Segmentation models are most frequently based on a U-Net structure. Multiple innovations have been added in terms of pre-processing or connection to analysis pipelines. Cardiac MRI is the most frequently segmented modality, which is due in part to the presence of publically available, moderately sized, computer vision competition datasets. Further progress in data availability, model explanation, and clinical integration are being pursued.SummaryThe task of cardiac anatomical segmentation has experienced massive strides forward within the past 5 years due to convolutional neural networks. These advances provide a basis for streamlining image analysis, and a foundation for further analysis both by computer and human systems. While technical advances are clear, clinical benefit remains nascent. Novel approaches may improve measurement precision by decreasing inter-reader variability and appear to also have the potential for larger-reaching effects in the future within integrated analysis pipelines. More... »

PAGES

18

References to SciGraph publications

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    URI

    http://scigraph.springernature.com/pub.10.1007/s12170-021-00678-4

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    http://dx.doi.org/10.1007/s12170-021-00678-4

    DIMENSIONS

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