Semi-supervised Nuclei Segmentation Based on Consistency Regularization Constraint
SHU Jianhua1, NIAN Fudong2, LÜ Gang2
1. School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei 230012 2. School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601
Abstract Aiming at the high cost of medical image data acquisition with high quality annotation, a semi-supervised nuclei segmentation algorithm based on consistency regularization constraint is proposed. Firstly, two master and slave networks with the same structure are constructed, and the same random initialization parameters are assigned to them. Then, the labeled and unlabeled training data are randomly selected to input into the master and slave networks. Regularization term is utilized to constrain the training of master and slave networks to keep the output results consistent. The parameters of master network are optimized by gradient descent method, and the parameters of the slave network are optimized by the exponential moving average of the parameters of master network in each iteration batches. Experiments on public datasets verify the effectiveness of the proposed algorithm.
Fund:Supported by National Natural Science Foundation of China(No.61902104), Key Project of Natural Science Foundation of Anhui University of Chinese Medicine (No.2019zrzd10), Scientific Research Development Foundation of Hefei University (No.19ZR15ZDA), Talent Research Foundation of Hefei University (No.18-19RC54)
Corresponding Authors:
NIAN Fudong, Ph.D., lecturer. His research interests include computer vision and multimedia computing.
About author:: SHU Jianhua, master, associate professor. Her research interests include medical image processing and data mining.
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2020, Vol. 33 
