Publication: Deep Upscale U-Net for automatic tongue segmentation
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Issued Date
2024-06-01
Resource Type
ISSN
01400118
eISSN
17410444
Scopus ID
2-s2.0-85185324399
Journal Title
Medical and Biological Engineering and Computing
Volume
62
Issue
6
Start Page
1751
End Page
1762
Rights Holder(s)
SCOPUS
Bibliographic Citation
Medical and Biological Engineering and Computing Vol.62 No.6 (2024) , 1751-1762
Suggested Citation
Kusakunniran W., Imaromkul T., Mongkolluksamee S., Thongkanchorn K., Ritthipravat P., Tuakta P., Benjapornlert P. Deep Upscale U-Net for automatic tongue segmentation. Medical and Biological Engineering and Computing Vol.62 No.6 (2024) , 1751-1762. 1762. doi:10.1007/s11517-024-03051-w Retrieved from: https://hdl.handle.net/20.500.14740/20639
Corresponding Author(s)
Other Contributor(s)
Abstract
Abstract: In a treatment or diagnosis related to oral health conditions such as oral cancer and oropharyngeal cancer, an investigation of tongue’s movements is a major part. In an automatic measurement of such movement, it must first start with a task of tongue segmentation. This paper proposes a solution of tongue segmentation based on a decoder-encoder CNN-based structure i.e., U-Net. However, it could suffer from a problem of feature loss in deep layers. This paper proposes a Deep Upscale U-Net (DU-UNET). An additional up-sampling of the feature map from a contracting path is concatenated to an upper layer of an expansive path, based on an original U-Net structure. The segmentation model is constructed by training DU-UNET on the two publicly available datasets, and transferred to the self-collected dataset of tongue images with five tongue postures which were recorded at a far distance from a camera under a real-world scenario. The proposed DU-UNET outperforms the other existing methods in our literature reviews, with accuracy of 99.2%, mean IoU of 97.8%, Dice score of 96.8%, and Jaccard score of 96.8%. Graphical abstract: (Figure presented.)