Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning
Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved..
OBJECTIVES: To develop a deep learning (DL) model for segmentation of the suprapatellar capsule (SC) and infrapatellar fat pad (IPFP) based on sagittal proton density-weighted images and to distinguish between three common types of knee synovitis.
MATERIALS AND METHODS: This retrospective study included 376 consecutive patients with pathologically confirmed knee synovitis (rheumatoid arthritis, gouty arthritis, and pigmented villonodular synovitis) from two institutions. A semantic segmentation model was trained on manually annotated sagittal proton density-weighted images. The segmentation results of the regions of interest and patients' sex and age were used to classify knee synovitis after feature processing. Classification by the DL method was compared to the classification performed by radiologists.
RESULTS: Data of the 376 patients (mean age, 42 ± 15 years; 216 men) were separated into a training set (n = 233), an internal test set (n = 93), and an external test set (n = 50). The automated segmentation model showed good performance (mean accuracy: 0.99 and 0.99 in the internal and external test sets). On the internal test set, the DL model performed better than the senior radiologist (accuracy: 0.86 vs. 0.79; area under the curve [AUC]: 0.83 vs. 0.79). On the external test set, the DL diagnostic model based on automatic segmentation performed as well or better than senior and junior radiologists (accuracy: 0.79 vs. 0.79 vs. 0.73; AUC: 0.76 vs. 0.77 vs. 0.70).
CONCLUSION: DL models for segmentation of SC and IPFD can accurately classify knee synovitis and aid radiologic diagnosis.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:31 |
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| Enthalten in: |
Academic radiology - 31(2024), 4 vom: 15. Apr., Seite 1518-1527 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Qizheng [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Completed 15.04.2024 Date Revised 15.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.acra.2023.10.036 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364434341 |
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| 520 | |a Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a OBJECTIVES: To develop a deep learning (DL) model for segmentation of the suprapatellar capsule (SC) and infrapatellar fat pad (IPFP) based on sagittal proton density-weighted images and to distinguish between three common types of knee synovitis | ||
| 520 | |a MATERIALS AND METHODS: This retrospective study included 376 consecutive patients with pathologically confirmed knee synovitis (rheumatoid arthritis, gouty arthritis, and pigmented villonodular synovitis) from two institutions. A semantic segmentation model was trained on manually annotated sagittal proton density-weighted images. The segmentation results of the regions of interest and patients' sex and age were used to classify knee synovitis after feature processing. Classification by the DL method was compared to the classification performed by radiologists | ||
| 520 | |a RESULTS: Data of the 376 patients (mean age, 42 ± 15 years; 216 men) were separated into a training set (n = 233), an internal test set (n = 93), and an external test set (n = 50). The automated segmentation model showed good performance (mean accuracy: 0.99 and 0.99 in the internal and external test sets). On the internal test set, the DL model performed better than the senior radiologist (accuracy: 0.86 vs. 0.79; area under the curve [AUC]: 0.83 vs. 0.79). On the external test set, the DL diagnostic model based on automatic segmentation performed as well or better than senior and junior radiologists (accuracy: 0.79 vs. 0.79 vs. 0.73; AUC: 0.76 vs. 0.77 vs. 0.70) | ||
| 520 | |a CONCLUSION: DL models for segmentation of SC and IPFD can accurately classify knee synovitis and aid radiologic diagnosis | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Diagnosis | |
| 650 | 4 | |a Knee | |
| 650 | 4 | |a Magnetic resonance imaging | |
| 650 | 4 | |a Synovitis | |
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| 700 | 1 | |a Xing, Xiaoying |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yongye |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Fangbo |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Ke |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Xiaoguang |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Shuqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Lang, Ning |e verfasserin |4 aut | |
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