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. 2021 Feb 7;21(4):1167.
doi: 10.3390/s21041167.

Deep Neural Network-Based Semantic Segmentation of Microvascular Decompression Images

Ruifeng Bai  1   2 , Shan Jiang  1 , Haijiang Sun  1 , Yifan Yang  1   2 , Guiju Li  1
Affiliations

Deep Neural Network-Based Semantic Segmentation of Microvascular Decompression Images

Ruifeng Bai et al. Sensors (Basel). .

Abstract

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+.

Keywords: DeepLabv3+; decoder structure; encoder structure; microvascular decompression image; semantic segmentation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The structure of the semantic-segmentation network for microvascular decompression images.
Figure 2
Figure 2
FDB (feature distillation block).
Figure 3
Figure 3
SRB (shallow residual block).
Figure 4
Figure 4
The Xception model is modified as follows: The second depth-wise separable convolution is replaced by the FDB in the residual block of input flow, middle flow, and exit flow.
Figure 5
Figure 5
Vertical structure of semantic segmentation of microvascular decompression images.
Figure 6
Figure 6
Examples of pre-processing.
Figure 7
Figure 7
Network-model-training loss curve.
Figure 8
Figure 8
Network-model-validation loss curve.
Figure 9
Figure 9
Experimental results and comparison with other methods.
Figure 10
Figure 10
Failure case.
See this image and copyright information in PMC

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