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We propose an automated framework for lung nodule segmentation from pulmonary CT scan using graph cut with a deep learned prior. The segmentation problem is formulated as a hybrid cost function minimization task, which combines a domain specific data term with a deep learned probability map. The proposed segmentation framework embodies the robustness of deep learning in object localization, while...
In this paper, we address the problem of automated pose classification and segmentation of the left ventricle (LV) in 2D echocardiographic images. For this purpose, we compare two complementary approaches. The first one is based on engineering ad-hoc features according to the traditional machine learning paradigm. Namely, we extract phase features to build an unsupervised LV pose estimator, as well...
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