Molecular Formula Image Segmentation with Shape Constraint Loss and Data Augmentation

Ruiqi Jia, Wentao Xie, Baole Wei, Guanren Qiao, Zonglin Yang, Xiaoqing Lyu, Zhi Tang

Abstract: The increasing demand for molecular formula image data leads to formidable pressure for researchers. Most existing image segmentation approaches can not be directly utilized for molecules, and how to improve the coverage fineness and generate a large amount of labeled training data is worthy of further exploration. To this end, we establish a deep learning based molecular formula image segmentation model (DL-MFS). Specifically, we design a shape constraint loss (SCL) function to refine the detection frame position and propose a rule-based molecular formula image data augmentation method for solving the bottleneck problem that the lack of training data. Experimental results demonstrate the effectiveness of the proposed segmentation model.