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Article: Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI

TitleDeep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI
Authors
KeywordsThigh muscle segmentation
Deep learning
Fat–water decomposition MRI
Quantitative MRI analysis
Issue Date2020
PublisherSpringerOpen. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/13244
Citation
Insights into Imaging, 2020, v. 11 n. 1, p. article no. 128 How to Cite?
AbstractBACKGROUND: Time-efficient and accurate whole volume thigh muscle segmentation is a major challenge in moving from qualitative assessment of thigh muscle MRI to more quantitative methods. This study developed an automated whole thigh muscle segmentation method using deep learning for reproducible fat fraction quantification on fat-water decomposition MRI. RESULTS: This study was performed using a public reference database (Dataset 1, 25 scans) and a local clinical dataset (Dataset 2, 21 scans). A U-net was trained using 23 scans (16 from Dataset 1, seven from Dataset 2) to automatically segment four functional muscle groups: quadriceps femoris, sartorius, gracilis and hamstring. The segmentation accuracy was evaluated on an independent testing set (3 × 3 repeated scans in Dataset 1 and four scans in Dataset 2). The average Dice coefficients between manual and automated segmentation were > 0.85. The average percent difference (absolute) in volume was 7.57%, and the average difference (absolute) in mean fat fraction (meanFF) was 0.17%. The reproducibility in meanFF was calculated using intraclass correlation coefficients (ICCs) for the repeated scans, and automated segmentation produced overall higher ICCs than manual segmentation (0.921 vs. 0.902). A preliminary quantitative analysis was performed using two-sample t test to detect possible differences in meanFF between 14 normal and 14 abnormal (with fat infiltration) thighs in Dataset 2 using automated segmentation, and significantly higher meanFF was detected in abnormal thighs. CONCLUSIONS: This automated thigh muscle segmentation exhibits excellent accuracy and higher reproducibility in fat fraction estimation compared to manual segmentation, which can be further used for quantifying fat infiltration in thigh muscles.
Persistent Identifierhttp://hdl.handle.net/10722/295261
ISSN
2019 Impact Factor: 3.579
2015 SCImago Journal Rankings: 1.004
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDing, J-
dc.contributor.authorCao, P-
dc.contributor.authorChang, HC-
dc.contributor.authorGao, Y-
dc.contributor.authorChan, HSS-
dc.contributor.authorVardhanabhuti, V-
dc.date.accessioned2021-01-11T13:57:36Z-
dc.date.available2021-01-11T13:57:36Z-
dc.date.issued2020-
dc.identifier.citationInsights into Imaging, 2020, v. 11 n. 1, p. article no. 128-
dc.identifier.issn1869-4101-
dc.identifier.urihttp://hdl.handle.net/10722/295261-
dc.description.abstractBACKGROUND: Time-efficient and accurate whole volume thigh muscle segmentation is a major challenge in moving from qualitative assessment of thigh muscle MRI to more quantitative methods. This study developed an automated whole thigh muscle segmentation method using deep learning for reproducible fat fraction quantification on fat-water decomposition MRI. RESULTS: This study was performed using a public reference database (Dataset 1, 25 scans) and a local clinical dataset (Dataset 2, 21 scans). A U-net was trained using 23 scans (16 from Dataset 1, seven from Dataset 2) to automatically segment four functional muscle groups: quadriceps femoris, sartorius, gracilis and hamstring. The segmentation accuracy was evaluated on an independent testing set (3 × 3 repeated scans in Dataset 1 and four scans in Dataset 2). The average Dice coefficients between manual and automated segmentation were > 0.85. The average percent difference (absolute) in volume was 7.57%, and the average difference (absolute) in mean fat fraction (meanFF) was 0.17%. The reproducibility in meanFF was calculated using intraclass correlation coefficients (ICCs) for the repeated scans, and automated segmentation produced overall higher ICCs than manual segmentation (0.921 vs. 0.902). A preliminary quantitative analysis was performed using two-sample t test to detect possible differences in meanFF between 14 normal and 14 abnormal (with fat infiltration) thighs in Dataset 2 using automated segmentation, and significantly higher meanFF was detected in abnormal thighs. CONCLUSIONS: This automated thigh muscle segmentation exhibits excellent accuracy and higher reproducibility in fat fraction estimation compared to manual segmentation, which can be further used for quantifying fat infiltration in thigh muscles.-
dc.languageeng-
dc.publisherSpringerOpen. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/13244-
dc.relation.ispartofInsights into Imaging-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectThigh muscle segmentation-
dc.subjectDeep learning-
dc.subjectFat–water decomposition MRI-
dc.subjectQuantitative MRI analysis-
dc.titleDeep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI-
dc.typeArticle-
dc.identifier.emailCao, P: caopeng1@hku.hk-
dc.identifier.emailChang, HC: hcchang@hku.hk-
dc.identifier.emailChan, HSS: sophehs@hku.hk-
dc.identifier.emailVardhanabhuti, V: varv@hku.hk-
dc.identifier.authorityCao, P=rp02474-
dc.identifier.authorityChang, HC=rp02024-
dc.identifier.authorityChan, HSS=rp02210-
dc.identifier.authorityVardhanabhuti, V=rp01900-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s13244-020-00946-8-
dc.identifier.pmid33252711-
dc.identifier.pmcidPMC7704819-
dc.identifier.hkuros320759-
dc.identifier.volume11-
dc.identifier.issue1-
dc.identifier.spagearticle no. 128-
dc.identifier.epagearticle no. 128-
dc.identifier.isiWOS:000595820500002-
dc.publisher.placeGermany-
dc.identifier.issnl1869-4101-

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