Generated Pseudo-Labels Guided by Background Skeletons for Overcoming Under-Segmentation in Overlapping Particle Objects
Jinshi Liu, Zhaohui Jiang, Ting Cao, Zhiwen Chen, Chaobo Zhang, Weihua Gui · IEEE Transactions on Circuits and Systems for Video Technology · 2022
Unlike general image segmentation, highly complex particle images have significant challenges in labeling and segmentation due to the information occlusion and texture disturbance. Aiming at the highly under-segmentation problem caused by complex particle image segmentation, this paper proposes a Semi-supervised Hybrid-training Particle Segmentation framework (SHPS) based on skeleton-guided pseudo-labels. First, a pre-trained model is obtained by training a popular segmentation algorithm on partially labeled data. Then, a Background Skeleton-guided Pseudo-label generation algorithm (BSP) is proposed to generate pseudo-labels closer to the ground truth in terms of structural integrity based on coarse segmentation. The final segmentation model is obtained by training a mixed dataset consisting of labeled data and pseudo-labels from another partition on the pre-trained model. The skeleton differences of pseudo-labels and coarse segmentation are added to the loss function. Experimental results show that our method achieves 84.4% accuracy on mIoU with uniform label data distribution, which is 2.1% higher than the accuracy of UNet and reduces the degree of under-segmentation.