Abstract
Mitral regurgitation (MR) is a frequent indication for valve surgery. One of its treatments, mitral valve performed with endoscopic video recordings, is a complex minimally invasive procedure which is facing the problem of data availability and data privacy. Therefore, the simulation cases are widely used to form surgery training and planning. However, the cross-domain gap may affect the performance significantly as Deep Learning methods rely heavily on data. We propose to develop an algorithm to reduce the domain gap between simulation and intra-operative cases. The task is to learn the distance and location information of the points and predict a series of 2D landmarks’ location, the coordinates of the landmark were both marked on real and simulate dataset by the AdaptOR Challenge organizer. Our work has merged the data from both domains by using a relation heatmap generation algorithm, which can generate a relation key point heatmap based on the distance measurement of landmarks and explicitly represent the geometric relation between landmarks. The MSE loss function is used to minimize the error between the ground-truth and predicted heatmaps. We test our methods on a challenge dataset, in which the model has achieved a good F1 score of 66.19%.
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Wang, J., Wang, H., Mu, R., Wang, L. (2021). Cross-Domain Landmarks Detection in Mitral Regurgitation. In: Engelhardt, S., et al. Deep Generative Models, and Data Augmentation, Labelling, and Imperfections. DGM4MICCAI DALI 2021 2021. Lecture Notes in Computer Science(), vol 13003. Springer, Cham. https://doi.org/10.1007/978-3-030-88210-5_12
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DOI: https://doi.org/10.1007/978-3-030-88210-5_12
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