Abnormal cell segmentation for lung pathological image based on denseblock and attention mechanism - CUI Wencheng; WANG Keli; SHAO Hong (智能科学与技术学报)
Aiming at the problems of unbalanced brightness of lung cell images and achieving accurate segmentation of abnormal cell contour difficultly, an abnormal cell segmentation model based on U-Net was proposed, which combined the dense connection mechanism and attention mechanism. Firstly, U-Net with encoder-decoder structure was used to segment abnormal cells. Secondly, the dense block was introduced into U-Net to improve the propagation ability between features and extract more characteristic information of abnormal cells. Finally, the attention mechanism was used to increase the weight of abnormal cell regions and reduce the interference of the imbalance of brightness to the model. The experimental results show that the IoU value and Dice similarity coefficient achieved by this method are 0.6928 and 0.8060, respectively. Compared with other models, this proposed method is able to segment low-contrast regions and abnormal cells with diverse shapes.