Abstract There are many challenges in current lung trachea segmentation including complex grayscale distribution of CT images, segmentation target pixel approximation, easy to cause over-segmentation, fewer lung trachea pixels, and difficult to get more target features, therefore, fine lung trachea is easy to be ignored. In this paper, we studied a lung trachea segmentation algorithm combining Unet network and attention mechanism, which a convolutional block attention model CBAM focusing on the channel domain and spatial domain was used in the attention mechanism, which improved the tracheal feature weights. In terms of loss function, for the problem of imbalance between positive and negative samples in the original data, this paper used the focal loss function to improve the standard cross-entropy loss function, so that the hard-to-classify samples got more attention in the training process. Finally, the isolated points were removed by eight connected domains judgment, and several larger connected domains were retained, i.e., the last pulmonary trachea part. Twenty-four sets of CT images and 43 sets of CTA images provided by the partner hospitals, totaling 26 157 slice images, were selected as the data set for segmentation experiments. The results showed that the segmentation accuracy reached 0.86, and the mean values of over-segmentation rate and under-segmentation rate were 0.28 and 0.39 respectively. After the ablation experiments of attention module and loss function, the accuracy, over-segmentation rate and under-segmentation rate before improvement were 0.81, 0.30 and 0.40, respectively, indicating the segmentation effect was inferior to the method proposed in this paper. Compared with other commonly used methods under the same conditions, the proposed method reached the highest accuracy rate under the condition that the over-segmentation rate, and under-segmentation rate were guaranteed to be unchanged. The above experiments proved the accuracy of the algorithm in this paper, and successfully solved the problem of inaccurate segmentation of fine trachea.
Zhang Ziming,Zhou Qinghua,Xue Hongsheng, et al. Research on Trachea Segmentation Algorithm Based on Unet+Attention from Chest CT Images[J]. Chinese Journal of Biomedical Engineering, 2024, 43(1): 60-69.
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