Lesion segmentation in lung CT scans using unsupervised adversarial learning.
Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Ne...
| Published in: | Medical & Biological Engineering & Computing Vol. 60; no. 11; pp. 3203 - 3216 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Nov2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159531217&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159531217 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2022 vid: 60 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159531217 159214298 159531217 NLM36125656 10.1007/s11517-022-02651-8 NLM36125656 159531217 ppf: 3203 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lesion segmentation in lung CT scans using unsupervised adversarial learning. aug: au: Sherwani, Moiz Khan Marzullo, Aldo De Momi, Elena Calimeri, Francesco affil: Department of Mathematics and Computer Science, University of Calabria, Rende, Italy sug: ab: Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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