Flow Convergence Area Estimation on In Vitro Color Flow Doppler Images Using Deep Learning
We present an automatic method to estimate flow rate through the orifice in in-vitro 2D color-flow Doppler echocardiographic images. Flow rate properties are important for the assessment of pathologies like mitral regurgitation. We expect this method to be transferable to in-vivo patient data. The method consists of two main parts: (a) detecting a bounding box which encloses aliasing contours and its surroundings (namely a region representative of flow convergence area), (b) application of Convolutional Neural Networks for regression to estimate the flow convergence area. Best result achieved is the 5% mean error for validation data which is from other experiments that were used for training. Given the small number of training data, this method shows promising results.
KeywordsDeep learning Color flow doppler Flow rate Mitral regurgitation
This research is funded by the Greek State Scholarships Foundation and European Social Fund.
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