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Reliable and robust hip dysplasia measurement with three-dimensional ultrasound and convolutional neural networks

University of British Columbia

Developmental Dysplasia of the Hip is one of the most common congenital disorders. Misdiagnosis leads to financial consequences and reduced quality of life. The current standard diagnostic technique involves imaging the hip with ultrasound and extracting metrics such as the α angle. This has been shown to be unreliable due to human error in probe positioning, leading to misdiagnosis. 3D ultrasound, being more robust to errors in probe positioning, has been introduced as a more reliable alternative. In this thesis, we aim to further improve the image processing techniques of the 3D ultrasound-based system, addressing three components: segmentation, metrics extraction, and adequacy classification. Segmentation in 3D is prohibitively slow when performed manually and introduces human error. Previous work introduced automatic segmentation techniques, but our observations indicate lack of accuracy and robustness with these techniques. We propose to use deep Convolutional Neural Network (CNN)s for improving the segmentation accuracy and consequently the reproducibility and robustness of dysplasia measurement. We show that 3D-U-Net achieves higher agreement with human labels compared to the state-of-the-art. For pelvis bone surface segmentation, we report mean DSC of 85% with 3D-U-Net vs. 26% with CSPS. For femoral head segmentation, we report mean CED Error of 1.42mm with 3D-U-Net vs. 3.90mm with the Random Forest Classifier. We implement methods for extracting α₃D, FHC₃D, and OCR dysplasia metrics using the improved segmentation. On a clinical set of 42 hips, we report inter-exam, intra-sonographer intraclass correlation coefficients of 87%, 84%, and 74% for these three metrics, respectively, beating the state-of-the-art. Qualitative observations show improved robustness and reduced failure rates. Previous work had explored automatic adequacy classification of hip 3D ultrasound, to provide clinicians with rapid point-of-care feedback on the quality of the scan. We revisit the originally proposed adequacy criteria and show that these criteria can be improved. Further, we show that 3D CNNs can be used to automate this task. Our best model shows good agreement with human labels, achieving an AROC of 84%. Ultimately, we aim to incorporate these models into a fully automatic, accurate, reliable, and robust system for hip dysplasia diagnosis.

Text

2020-03-10

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/73709

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/