LUNG NODULE SEGMENTATION USING DEEP LEARNED PRIOR BASED GRAPH CUT

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 retaining the hallmark of traditional segmentation models in addressing the morphological intricacies of elaborate objects. The proposed solution offers more than 20% performance improvement over a contemporary data driven model, and also outperforms traditional graph cuts especially in situations where model initialization is slightly inaccurate.