Transfer Shape Modeling Towards High-throughput Microscopy Image Segmentation.

In order to deal with ambiguous image appearances in cell segmentation, high-level shape modeling has been introduced to delineate cell boundaries. However, shape modeling usually requires sufficient annotated training shapes, which are often labor intensive or unavailable. Meanwhile, when applying the model to different datasets, it is necessary to repeat the tedious annotation process to generate enough training data, and this will significantly limit the applicability of the model. In this paper, we propose to transfer shape modeling learned from an existing but different dataset (e.g. lung cancer) to assist cell segmentation in a new target dataset (e.g. skeletal muscle) without expensive manual annotations. Considering the intrinsic geometry structure of cell shapes, we incorporate the shape transfer model into a sparse representation framework with a manifold embedding constraint, and provide an efficient algorithm to solve the optimization problem. The proposed algorithm is tested on multiple microscopy image datasets with different tissue and staining preparations, and the experiments demonstrate its effectiveness.