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In this paper we introduce a novel and accurate optimisation method forsegmentation of cardiac MR (CMR) images in patients with pulmonary hypertension(PH). The proposed method explicitly takes into account the image featureslearned from a deep neural network. To this end, we estimate simultaneousprobability maps over region and edge locations in CMR images using a fullyconvolutional network. Due to the distinct morphology of the heart in patientswith PH, these probability maps can then be incorporated in a single nestedlevel set optimisation framework to achieve multi-region segmentation with highefficiency. The proposed method uses an automatic way for level setinitialisation and thus the whole optimisation is fully automated. Wedemonstrate that the proposed deep nested level set (DNLS) method outperformsexisting state-of-the-art methods for CMR segmentation in PH patients.

Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension