The International Workshop on Osteoarthritis Imaging Knee MRI                     Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a                     Standardized Dataset

Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. Level of Evidence: 5 Technical Efficacy Stage: 2

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“…A study of a segmentation challenge organized in 2019 showed that simpler models often perform on a par with larger and more complex models. 104…”

Section: Opportunitiesmentioning

confidence: 99%

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices

Chaudhari

Sandino

Cole

et al. 2020

Magnetic Resonance Imaging

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“…10). In the IWOAI 2019 knee segmentation challenge mentioned earlier, 8 the best performing meniscus segmentation model showed a DSC of 0.88. Using a fully automatic segmentation algorithm for the menisci based on 3D MRI acquisitions, Xu et al demonstrated that higher baseline meniscal volume and a stronger decrease in meniscal volume over time are associated with developing radiographic knee OA after 30 months in overweight and obese women.…”

Section: Automated Segmentation Morphological Analysis and Meniscal Lesion Detectionmentioning

confidence: 94%

“…7 In the 2019 International Workshop on Osteoarthritis Imaging (IWOAI) knee segmentation challenge, a standardized partition of the 3D DESS data from the Osteoarthritis Initiative was used to compare six different segmentation algorithms. 8 The highest Dice similarity coefficients (DSCs), a common metric to assess segmentation performance, for segmentation of femoral, tibial, and patellar cartilage were 0.90, 0.89, and 0.86, respectively.…”

Section: Articular Cartilage Segmentationmentioning

confidence: 99%

3D MRI in Osteoarthritis

Oei

Zadelhoff

Eijgenraam

et al. 2021

Semin Musculoskelet Radiol

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Osteoarthritis (OA) is among the top 10 burdensome diseases, with the knee the most affected joint. Magnetic resonance imaging (MRI) allows whole-knee assessment, making it ideally suited for imaging OA, considered a multitissue disease. Three-dimensional (3D) MRI enables the comprehensive assessment of OA, including quantitative morphometry of various joint tissues. Manual tissue segmentation on 3D MRI is challenging but may be overcome by advanced automated image analysis methods including artificial intelligence (AI). This review presents examples of the utility of 3D MRI for knee OA, focusing on the articular cartilage, bone, meniscus, synovium, and infrapatellar fat pad, and it highlights several applications of AI that facilitate segmentation, lesion detection, and disease classification.

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“…U-Net has demonstrated high performance for lung segmentation from radiographs and bone segmentation from MRI in addition to many other tasks 42,43 . In this study, we did not compare different CNN architectures, but the results of a recent segmentation challenge showed similar performance between three-dimensional V-Net and three-dimensional U-Net models for a knee cartilage MRI segmentation task 44 . The application of CNN's and deep learning into medical imaging analysis has been a major advancement in the field, leading to significant gains in segmentation performance across multiple medical imaging applications (for a comprehensive review see Hesamian et al (2019)) 45 .…”

Section: Muscle Mfi (%)mentioning

confidence: 98%

Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions

Weber

Abbott

Bojilov

et al. 2021

Sci Rep

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Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The quantification of MFI requires time-consuming and rater-dependent manual segmentation techniques. A convolutional neural network (CNN) model was trained to segment seven cervical spine muscle groups (left and right muscles segmented separately, 14 muscles total) from Dixon MRI scans (n = 17, 17 scans < 2 weeks post motor vehicle collision (MVC), and 17 scans 12 months post MVC). The CNN MFI measures demonstrated high test reliability and accuracy in an independent testing dataset (n = 18, 9 scans < 2 weeks post MVC, and 9 scans 12 months post MVC). Using the CNN in 84 participants with scans < 2 weeks post MVC (61 females, 23 males, age = 34.2 ± 10.7 years) differences in MFI between the muscle groups and relationships between MFI and sex, age, and body mass index (BMI) were explored. Averaging across all muscles, females had significantly higher MFI than males (p = 0.026). The deep cervical muscles demonstrated significantly greater MFI than the more superficial muscles (p < 0.001), and only MFI within the deep cervical muscles was moderately correlated to age (r > 0.300, p ≤ 0.001). CNN’s allow for the accurate and rapid, quantitative assessment of the composition of the architecturally complex muscles traversing the cervical spine. Acknowledging the wider reports of MFI in cervical spine disorders and the time required to manually segment the individual muscles, this CNN may have diagnostic, prognostic, and predictive value in disorders of the cervical spine.

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The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Cited by 55 publications

References 26 publications

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices

3D MRI in Osteoarthritis

Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions

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