Weakly Supervised Instance Segmentation of SEM Image via Synthetic Data
Yunfeng Wang, Xiaoqin Tang, Jingchuan Fan, Guoqiang Xiao · 2020
Instance segmentation of scanning electron microscope images provides useful information for quantitative analysis of particle morphometry and distribution that contributes to various biomedical research such as the phenotyping of drug delivery systems. Compared to the conventional segmentation methodologies, the learning-based approaches stand out, benefiting from the prosperous development of artificial intelligence. However, most of the current learning-based segmentation methods require sufficient manually annotated training data, which is considered to be laborious. To alleviate this problem, we present a novel weakly supervised framework for instance segmentation on scanning electron microscope images. In the proposed framework, only one instance from each raw image is manually labeled to generate a synthesized dataset, which will be further used to select the training set. With the weakly annotated training dataset, the instance segmentation network is trained and applied to segment the particles of raw testing images. Based on our experimental results, the trained network gains 75% recall and 74% average precision on the tested images, which is seen as a reasonable performance considering the data complexity in our research. The overall experiments demonstrate that the proposed weakly supervised framework is able to provide an efficient solution to the instance segmentation of biomedical images.