Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI

Overview

Journal J Imaging Inform Med

Publisher Springer Nature

Specialty Radiology

Date 2024 Jul 17

PMID 39020156

Authors

Kexin Jiang

Yuhan Xie

Xintao Zhang

Xinru Zhang

Beibei Zhou

Mianwen Li

Yanjun Chen

Jiaping Hu

Zhiyong Zhang

Shaolong Chen

Keyan Yu

Changzhen Qiu

Xiaodong Zhang

Affiliations

Soon will be listed here.

Abstract

Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supervised networks based on MRI images. In this retrospective study, data were from the osteoarthritis initiative (OAI). The MR images were reconstructed using a sagittal intermediate-weighted fat-suppressed turbo spin-echo sequence. (1) We used 130 knees from the OAI to develop the LGSA-UNet model which fuses the features of adjacent slices and adjusts the blocks in Siam to enable the central slice to obtain rich contextual information. (2) One thousand seven hundred and fifty-six knees from the OAI were included to establish segmentation and classification models. The segmentation model achieved a DICE coefficient ranging from 0.84 to 0.93. The AUC values ranged from 0.85 to 0.95 in the binary models. The accuracy for the three types of menisci (normal, tear, and maceration) ranged from 0.60 to 0.88. Furthermore, 206 knees from the orthopedic hospital were used as an external validation data set to evaluate the performance of the model. The segmentation and classification models still performed well on the external validation set. To compare the diagnostic performances between the deep learning (DL) models and radiologists, the external validation sets were sent to two radiologists. The binary classification model outperformed the diagnostic performance of the junior radiologist (0.82-0.87 versus 0.74-0.88). This study highlights the potential of DL in knee meniscus segmentation and injury classification which can help improve diagnostic efficiency.

References

Englund M, Guermazi A, Lohmander S . The role of the meniscus in knee osteoarthritis: a cause or consequence?. Radiol Clin North Am. 2009; 47(4):703-12. DOI: 10.1016/j.rcl.2009.03.003. View

Greis P, Bardana D, Holmstrom M, Burks R . Meniscal injury: I. Basic science and evaluation. J Am Acad Orthop Surg. 2002; 10(3):168-76. DOI: 10.5435/00124635-200205000-00003. View

Bien N, Rajpurkar P, Ball R, Irvin J, Park A, Jones E . Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS Med. 2018; 15(11):e1002699. PMC: 6258509. DOI: 10.1371/journal.pmed.1002699. View

Tack A, Shestakov A, Ludke D, Zachow S . A Multi-Task Deep Learning Method for Detection of Meniscal Tears in MRI Data from the Osteoarthritis Initiative Database. Front Bioeng Biotechnol. 2021; 9:747217. PMC: 8675251. DOI: 10.3389/fbioe.2021.747217. View

Markes A, Hodax J, Ma C . Meniscus Form and Function. Clin Sports Med. 2019; 39(1):1-12. DOI: 10.1016/j.csm.2019.08.007. View

Makris E, Hadidi P, Athanasiou K . The knee meniscus: structure-function, pathophysiology, current repair techniques, and prospects for regeneration. Biomaterials. 2011; 32(30):7411-31. PMC: 3161498. DOI: 10.1016/j.biomaterials.2011.06.037. View

Jeon U, Kim H, Hong H, Wang J . Automatic Meniscus Segmentation Using Adversarial Learning-Based Segmentation Network with Object-Aware Map in Knee MR Images. Diagnostics (Basel). 2021; 11(9). PMC: 8472118. DOI: 10.3390/diagnostics11091612. View

Astuto B, Flament I, Namiri N, Shah R, Bharadwaj U, Link T . Automatic Deep Learning-assisted Detection and Grading of Abnormalities in Knee MRI Studies. Radiol Artif Intell. 2021; 3(3):e200165. PMC: 8166108. DOI: 10.1148/ryai.2021200165. View

Zhou Z, Zhao G, Kijowski R, Liu F . Deep convolutional neural network for segmentation of knee joint anatomy. Magn Reson Med. 2018; 80(6):2759-2770. PMC: 6342268. DOI: 10.1002/mrm.27229. View

10.

Norman B, Pedoia V, Majumdar S . Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry. Radiology. 2018; 288(1):177-185. PMC: 6013406. DOI: 10.1148/radiol.2018172322. View

11.

Guan S, Khan A, Sikdar S, Chitnis P . Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal. IEEE J Biomed Health Inform. 2019; 24(2):568-576. DOI: 10.1109/JBHI.2019.2912935. View

12.

Adams B, Houston M, Cameron K . The Epidemiology of Meniscus Injury. Sports Med Arthrosc Rev. 2021; 29(3):e24-e33. DOI: 10.1097/JSA.0000000000000329. View

13.

Fritz B, Marbach G, Civardi F, Fucentese S, Pfirrmann C . Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference. Skeletal Radiol. 2020; 49(8):1207-1217. PMC: 7299917. DOI: 10.1007/s00256-020-03410-2. View

14.

McBee M, Awan O, Colucci A, Ghobadi C, Kadom N, Kansagra A . Deep Learning in Radiology. Acad Radiol. 2018; 25(11):1472-1480. DOI: 10.1016/j.acra.2018.02.018. View

15.

Nelson E, LaPrade R . The anterior intermeniscal ligament of the knee. An anatomic study. Am J Sports Med. 2000; 28(1):74-6. DOI: 10.1177/03635465000280012401. View

16.

Englund M, Roemer F, Hayashi D, Crema M, Guermazi A . Meniscus pathology, osteoarthritis and the treatment controversy. Nat Rev Rheumatol. 2012; 8(7):412-9. DOI: 10.1038/nrrheum.2012.69. View

17.

Byra M, Wu M, Zhang X, Jang H, Ma Y, Chang E . Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U-Net with transfer learning. Magn Reson Med. 2019; 83(3):1109-1122. PMC: 6879791. DOI: 10.1002/mrm.27969. View

18.

Awan M, Mohd Rahim M, Salim N, Mohammed M, Garcia-Zapirain B, Abdulkareem K . Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach. Diagnostics (Basel). 2021; 11(1). PMC: 7826961. DOI: 10.3390/diagnostics11010105. View

19.

Gaj S, Yang M, Nakamura K, Li X . Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks. Magn Reson Med. 2019; 84(1):437-449. DOI: 10.1002/mrm.28111. View

20.

Mordecai S, Al-Hadithy N, Ware H, Gupte C . Treatment of meniscal tears: An evidence based approach. World J Orthop. 2014; 5(3):233-41. PMC: 4095015. DOI: 10.5312/wjo.v5.i3.233. View