Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93657
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | en_US |
dc.contributor | Department of Biomedical Engineering | en_US |
dc.creator | Banerjee, S | en_US |
dc.creator | Lyu, J | en_US |
dc.creator | Huang, Z | en_US |
dc.creator | Leung, HFF | en_US |
dc.creator | Lee, TTY | en_US |
dc.creator | Yang, D | en_US |
dc.creator | Su, S | en_US |
dc.creator | Zheng, Y | en_US |
dc.creator | Ling, SH | en_US |
dc.date.accessioned | 2022-07-19T10:02:43Z | - |
dc.date.available | 2022-07-19T10:02:43Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/93657 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Banerjee, S., Lyu, J., Huang, Z., Leung, H. F. F., Lee, T. T. Y., Yang, D., ... & Ling, S. H. (2021). Light-convolution dense selection U-Net (LDS U-Net) for ultrasound lateral bony feature segmentation. Applied Sciences, 11(21), 10180 is available at https://doi.org/10.3390/app112110180 | en_US |
dc.subject | Lateral bony feature | en_US |
dc.subject | Depthwise separable convolution | en_US |
dc.subject | Segmentation | en_US |
dc.subject | Scoliosis | en_US |
dc.subject | Ultrasound | en_US |
dc.subject | U-Net | en_US |
dc.title | Light-Convolution Dense Selection U-Net (LDS U-Net) for ultrasound lateral bony feature segmentation | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 11 | en_US |
dc.identifier.issue | 21 | en_US |
dc.identifier.doi | 10.3390/app112110180 | en_US |
dcterms.abstract | Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Applied sciences, Nov. 2021, v. 11, no. 21, 10180 | en_US |
dcterms.isPartOf | Applied sciences | en_US |
dcterms.issued | 2021-11 | - |
dc.identifier.isi | WOS:000719522000001 | - |
dc.identifier.scopus | 2-s2.0-85118386840 | - |
dc.identifier.eissn | 2076-3417 | en_US |
dc.identifier.artn | 10180 | en_US |
dc.description.validate | 202207 bckw | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a1544-n02 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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applsci-11-10180-v3.pdf | 49.14 MB | Adobe PDF | View/Open |
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