An anchor-free instance segmentation method for cells based on mask contour

Qi Chen, Huihuang Zhang, Qianwei Zhou, Qiu Guan, Haigen Hu

Abstract: Detection and segmentation of cells can be of great significance to the further quantitative analysis of biomedical research in the field of biomedical engineering. Especially, it is a serious challenging task for some microscope imaging devices with limited resources owing to a large number of learning parameters and computational burden when using the detect-then-segment two-stage strategy. In this work, an anchor-free instance segmentation method is proposed for cells based on mask contour. Specifically, an anchor-free network framework is firstly designed for instance segmentation by replacing the standard convolution in the contour generation branch with a deformable convolution. Then, these key contour points are linked to generate coarse contours of cells by using a Graham algorithm. Thirdly, the obtained coarse contours are used for regressing and approaching the ground truths in polar coordinates under the supervision of the Mask loss function. Finally, a series of comparison experiments are conducted to verify the effectiveness of the proposed methods on various datasets. The results show that the proposed method can obtain a better trade-off between recognition performance and computing efficiency, and it can surpass the existing one-stage SOTA methods in the Dice coefficient while maintaining higher computational efficiency on different datasets. Even with a two-stage instance segmentation method like Mask R-CNN, the proposed method can only obtain slightly lower Dice coefficients but with much higher FPS.