Web: http://arxiv.org/abs/2112.02102

May 9, 2022, 1:10 a.m. | Nathan Painchaud, Nicolas Duchateau, Olivier Bernard, Pierre-Marc Jodoin

cs.CV updates on arXiv.org arxiv.org

Convolutional neural networks (CNN) have demonstrated their ability to
segment 2D cardiac ultrasound images. However, despite recent successes
according to which the intra-observer variability on end-diastole and
end-systole images has been reached, CNNs still struggle to leverage temporal
information to provide accurate and temporally consistent segmentation maps
across the whole cycle. Such consistency is required to accurately describe the
cardiac function, a necessary step in diagnosing many cardiovascular diseases.
In this paper, we propose a framework to learn the 2D+time …

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