Cell Segmentation Proposal Network for Microscopy Image Analysis

Abstract

Accurate cell segmentation is vital for the development of reliable microscopy image analysis methods. It is a very challenging problem due to low contrast, weak boundaries, and conjoined and overlapping cells; producing many ambiguous regions, which lower the performance of automated segmentation methods. Cell proposals provide an efficient way of exploiting both spatial and temporal context, which can be very helpful in many of these ambiguous regions. However, most proposal based microscopy image analysis methods rely on fairly simple proposal generation stage, limiting their performance. In this paper, we propose a convolutional neural network based method which provides cell segmentation proposals, which can be used for cell detection, segmentation and tracking. We evaluate our method on datasets from histology, fluorescence and phase contrast microscopy and show that it outperforms state of the art cell detection and segmentation methods.