Robust selection-based sparse shape model for lung cancer image segmentation

Accurate cellular level segmentation of lung cancer is the prerequisite to extract objective morphological features in digitized pathology specimens. It is of great importance for image-guided diagnosis and prognosis. However, it is challenging to correctly and robustly segment cells in lung cancer images due to cell occlusion or touching, intracellular inhomogeneity, background clutter, etc. In this paper, we present a novel segmentation algorithm combining a robust selection-based sparse shape model (top-down) and an efficient local repulsive balloon snake deformable model (bottom-up) to tackle these challenges. The algorithm has been extensively tested on 62 cases with over 6000 tumor cells. We experimentally demonstrate that the proposed algorithm can produce better performance than other state-of-the-art methods.