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. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: Curr Cardiovasc Risk Rep. 2021 Aug 4;15(9):18. doi: 10.1007/s12170-021-00678-4

Figure 1: Summary figure.

Figure 1:

Semantic segmentation exists within computer vision, and more recently has used deep learning techniques to augment traditional methods. While classical segmentation defined clinically relevant regions, anatomical segmentation follows anatomical boundaries, and enables more precise definitions of structure and function, which may be integrated into analysis pipelines. The 5-year literature survey shows recent growth in AI and cardiac segmentation, with MRI as the most common modality. External dataset use was most frequent with the ACDC 2017 set, and U-Net was most commonly used for segmentation. MRI: Magnetic resonance imaging, CT: Computed Tomography, TTE: Transthoracic echocardiography, CXR: Chest X-ray, CNN: Convolutional neural network, FCN: Fully convolutional network, ACDC 2017: Medical Image Computing and Computer Assisted Interventions (MICCAI) 2017 Automated Cardiac Diagnosis Challenge, MICCAI 2009: MICCAI/STACOM Sunnybrook Cardiac MR 2009 Left Ventricle Segmentation Challenge, LV Seg 2011: MICCAI/STACOM Left Ventricle Segmentation Dataset and Challenge from 2011