The impact of data augmentation and transfer learning on the performance of deep learning models for the segmentation of the hip on 3D magnetic resonance images

Montin, Eros; Deniz, Cem M.; Kijowski, Richard; Youm, Thomas; Lattanzi, Riccardo

doi:10.1016/j.imu.2023.101444

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Conventional augmentation techniques include geometric transformations such as translations, rotations and flipping and intensity transformations through image filtering and noise addition [8,9]. For skeletal segmentation, the use of conventional techniques, more specifically roto-translational augmentation, leads to improved segmentation results of the hip joint [10].…”

Section: Introductionmentioning

confidence: 99%

Sample Size Effect on Musculoskeletal Segmentation: How Low Can We Go?

Huysentruyt,

Van den Borre,

Lazendić

et al. 2024

Electronics

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Convolutional Neural Networks have emerged as a predominant tool in musculoskeletal medical image segmentation. It enables precise delineation of bone and cartilage in medical images. Recent developments in image processing and network architecture desire a reevaluation of the relationship between segmentation accuracy and the amount of training data. This study investigates the minimum sample size required to achieve clinically relevant accuracy in bone and cartilage segmentation using the nnU-Net methodology. In addition, the potential benefit of integrating available medical knowledge for data augmentation, a largely unexplored opportunity for data preprocessing, is investigated. The impact of sample size on the segmentation accuracy of the nnU-Net is studied using three distinct musculoskeletal datasets, including both MRI and CT, to segment bone and cartilage. Further, the use of model-informed augmentation is explored on two of the above datasets by generating new training samples implementing a shape model-informed approach. Results indicate that the nnU-Net can achieve remarkable segmentation accuracy with as few as 10–15 training samples on bones and 25–30 training samples on cartilage. Model-informed augmentation did not yield relevant improvements in segmentation results. The sample size findings challenge the common notion that large datasets are necessary to obtain clinically relevant segmentation outcomes in musculoskeletal applications.

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Section: Introductionmentioning

confidence: 99%

Sample Size Effect on Musculoskeletal Segmentation: How Low Can We Go?

Huysentruyt,

Van den Borre,

Lazendić

et al. 2024

Electronics

View full text Add to dashboard Cite

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Radiomics features outperform standard radiological measurements in detecting femoroacetabular impingement on three‐dimensional magnetic resonance imaging

Montin,

Kijowski,

Youm

et al. 2024

Journal Orthopaedic Research

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Femoroacetabular impingement (FAI) is a cause of hip pain and can lead to hip osteoarthritis. Radiological measurements obtained from radiographs or magnetic resonance imaging (MRI) are normally used for FAI diagnosis, but they require time‐consuming manual interaction, which limits accuracy and reproducibility. This study compares standard radiologic measurements against radiomics features automatically extracted from MRI for the identification of FAI patients versus healthy subjects. Three‐dimensional Dixon MRI of the pelvis were retrospectively collected for 10 patients with confirmed FAI and acquired for 10 healthy subjects. The femur and acetabulum were segmented bilaterally and associated radiomics features were extracted from the four MRI contrasts of the Dixon sequence (water‐only, fat‐only, in‐phase, and out‐of‐phase). A radiologist collected 21 radiological measurements typically used in FAI. The Gini importance was used to define 9 subsets with the most predictive radiomics features and one subset for the most diagnostically relevant radiological measurements. For each subset, 100 Random Forest machine learning models were trained with different data splits and fivefold cross‐validation to classify healthy subjects versus FAI patients. The average performance among the 100 models was computed for each subset and compared against the performance of the radiological measurements. One model trained using the radiomics features datasets yielded 100% accuracy in the detection of FAI, whereas all other radiomics features exceeded 80% accuracy. Radiological measurements yielded 74% accuracy, consistent with previous work. The results of this preliminary work highlight for the first time the potential of radiomics for fully automated FAI diagnosis.

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The impact of data augmentation and transfer learning on the performance of deep learning models for the segmentation of the hip on 3D magnetic resonance images

Cited by 2 publications

References 24 publications

Sample Size Effect on Musculoskeletal Segmentation: How Low Can We Go?

Sample Size Effect on Musculoskeletal Segmentation: How Low Can We Go?

Radiomics features outperform standard radiological measurements in detecting femoroacetabular impingement on three‐dimensional magnetic resonance imaging

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