Segmentation of the aortic root on angiography images
The article describes an approach based on neural network modeling, which is of great interest for segmenting different anatomical structures during TAVI. Given the complexity of analyzing large volumes of data for cardiac surgeons, the article pays special attention to the automatic analysis of medical data, training, and comparison of modem neural networks. Fifty neural networks were thoroughly examined and tested for predicting aortic root masks (encoders: U-net, U-net++, Linknet, FPN, DeepLabV3+ and decoders: Efficientnet-b0, Efficientnet-b1, Resnext50, Resnet34, Regnetx32). During the training and testing phases, the FPN Efficientnet-b0 cascade architecture demonstrated the best prediction accuracy with metrics IOU 0.771 and Dice 0.870. The conducted study shows that the proposed approach based on neural network cascades, which focuses not on detecting key points but on creating segmentation masks, allows for real-time prediction of the aortic valve and delivery system location, thereby facilitating correct valve positioning during TAVI. Contribution of the authors: the authors contributed equally to this article. The authors declare no conflicts of interests.
Keywords
segmentation,
transcatheter aortic valve replacement,
deep learning - CNN,
medical image analysis,
aortographyAuthors
| Gerget Olga M. | V.A. Trapeznikov Institute of Control Sciences, RAS | olgagerget@mail.ru |
| Laptev Nikita V. | Siberian State Medical University | laptev.nv@ssmu.ru |
| Chernyavsky Michael A. | National Medical Research Center named after V.A. Almazov | machern @mail.ru |
Всего: 3
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