Bai Ruifeng, Jiang Shan, Sun Haijiang, Yang Yifan, Li Guiju
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2021 Feb 7;21(4):1167. doi: 10.3390/s21041167.
Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial nerve segmentation based on true-color medical images is in urgent need and has good research and development prospects. We have extended the current state-of-the-art semantic-segmentation network DeepLabv3+ and used it as the basic framework. First, the feature distillation block (FDB) was introduced into the encoder structure to refine the extracted features. In addition, the atrous spatial pyramid pooling (ASPP) module was added to the decoder structure to enhance the retention of feature and boundary information. The proposed model was trained by fine tuning and optimizing the relevant parameters. Experimental results show that the encoder structure has better performance in feature refinement processing, improving target boundary segmentation precision, and retaining more feature information. Our method has a segmentation accuracy of 75.73%, which is 3% better than DeepLabv3+.
图像语义分割在卫星遥感、医疗、智能交通和虚拟现实等领域的应用越来越广泛。然而，在医学领域，基于真彩色医学图像的脑血管和颅神经分割研究亟待开展，且具有良好的研发前景。我们对当前最先进的语义分割网络DeepLabv3+进行了扩展，并将其用作基本框架。首先，将特征蒸馏模块（FDB）引入编码器结构以细化提取的特征。此外，在解码器结构中添加了空洞空间金字塔池化（ASPP）模块，以增强特征和边界信息的保留。通过微调并优化相关参数对所提出的模型进行训练。实验结果表明，编码器结构在特征细化处理方面具有更好的性能，提高了目标边界分割精度，并保留了更多的特征信息。我们的方法分割准确率为75.73%，比DeepLabv3+高出3%。
