Affiliations: [a] College of Software Engineering, Xinjiang University, Urumqi, Xinjiang, China | [b] Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, Xinjiang, China | [c] College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China | [d] People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China | [e] China Xinjiang Key Laboratory of Dermatology Research (XJYS1707), Urumqi, Xinjiang, China | [f] College of Computer Science, Sichuan University, Chengdu, Sichuan, China
Correspondence:
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Corresponding author: Shengwei Tian, College of Software Engineering, Xinjiang University, Urumqi, Xinjiang, China. E-mail: tianshengwei@163.com.
Abstract: BACKGROUND: The results of medical image segmentation can provide reliable evidence for clinical diagnosis and treatment. The U-Net proposed previously has been widely used in the field of medical image segmentation. Its encoder extracts semantic features of different scales at different stages, but does not carry out special processing for semantic features of each scale. OBJECTIVE: To improve the feature expression ability and segmentation performance of U-Net, we proposed a feature supplement and optimization U-Net (FSOU-Net). METHODS: First, we put forward the view that semantic features of different scales should be treated differently. Based on this view, we classify the semantic features automatically extracted by encoders into two categories: shallow semantic features and deep semantic features. Then, we propose the shallow feature supplement module (SFSM), which obtains fine-grained semantic features through up-sampling to supplement the shallow semantic information. Finally, we propose the deep feature optimization module (DFOM), which uses the expansive convolution of different receptive fields to obtain multi-scale features and then performs multi-scale feature fusion to optimize the deep semantic information. RESULTS: The proposed model is experimented on three medical image segmentation public datasets, and the experimental results prove the correctness of the proposed idea. The segmentation performance of the model is higher than the advanced models for medical image segmentation. Compared with baseline network U-NET, the main index of Dice index is 0.75% higher on the RITE dataset, 2.3% higher on the Kvasir-SEG dataset, and 0.24% higher on the GlaS dataset. CONCLUSIONS: The proposed method can greatly improve the feature representation ability and segmentation performance of the model.
Keywords: Deep learning algorithm, medical image analysis, semantic segmentation, multi-scale, convolutional neural networks
