Confidence-based Training for Clinical Data Uncertainty in Image-based Prediction of Cardiac Ablation Targets

Abstract : Ventricular radio-frequency ablation (RFA) can have a critical impact on preventing sudden cardiac arrest but is challenging due to a highly complex arrhythmogenic substrate. This work aims to identify local image characteristics capable of predicting the presence of local abnormal ventricular activities (LAVA). This can allow, pre-operatively and non-invasively, to improve and accelerate the procedure. To achieve this, intensity and texture-based local image features are computed and random forests are used for classification. However using machine-learning approaches on such complex multimodal data can prove difficult due to the inherent errors in the training set. In this manuscript we present a detailed analysis of these error sources due in particular to catheter motion and the data fusion process. We derived a principled analysis of confidence impact on classification. Moreover, we demonstrate how formal integration of these uncertainties in the training process improves the algorithm's performance, opening up possibilities for non-invasive image-based prediction of RFA targets.
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Communication dans un congrès
bigMCV Workshop MICCAI 2014, Sep 2014, Boston, United States. 2014
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https://hal.inria.fr/hal-01069085
Contributeur : Rocio Cabrera Lozoya <>
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Dernière modification le : mercredi 30 mai 2018 - 13:56:03
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Rocio Cabrera Lozoya, Jan Margeta, Loic Le Folgoc, Yuki Komatsu, Berte Benjamin, et al.. Confidence-based Training for Clinical Data Uncertainty in Image-based Prediction of Cardiac Ablation Targets. bigMCV Workshop MICCAI 2014, Sep 2014, Boston, United States. 2014. 〈hal-01069085〉

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