Abstract

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Title:: Neural network-based multi-level supervision and attention mechanism algorithm for brain glioma CTC segmentation

Author(s):: YUAN Hongjie1; YANG Yan1; ZHANG Dong1; YANG Shuang1; 2; 1. School of Physics and Technology, Wuhan University, Wuhan 430072, China 2. School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China

Keywords:: Keywords: glioma circulating tumor cell multi-level supervision convolutional block attention module small target segmentation

Abstract:: Abstract: In order to improve the segmentation accuracy of glioma circulating tumor cells (CTC) and solve the problems of difficulties in distinguishing boundaries with the naked eye, small target ratio and cumbersome operation process in manual segmentation, an end-to-end pixel-level segmentation algorithm is proposed. In view of data features, the proposed algorithm utilizes a hybrid loss function based on a multi-level supervision mechanism to improve the intersection-over-union between the prediction area and the ground truth area, and iterate the network converging in the direction of predicting the right numbers of targets. Then, the convolutional block attention module is put in every layer of the proposed network enables the network to focus on learning data features at the spatial and channel dimensions, thereby further improving the prediction accuracy. Finally, the proposed algorithm can segment the nucleus and cytoplasm by one network model through hybrid training, which simples the process of training. The experimental results showed that compared with U-Net network, the proposed segmentation algorithm has improved in terms of recall rate, precision and Dice coefficient. The above-mentioned indexes are 92.20%, 86.56%, 88.27% for cell nucleus segmentation, and 89.33%, 85.31%, 86.33% for cytoplasm segmentation.