Automatic detection and localization of bone erosion in hand HR-pQCT
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Automatic detection and localization of bone erosion in hand HR-pQCT. / Ren, Jintao; Arash Moaddel, H.; Hauge, Ellen M.; Keller, Kresten K.; Jensen, Rasmus K.; Lauze, François.
Medical Imaging 2019: Computer-Aided Diagnosis. ed. / Kensaku Mori; Horst K. Hahn. SPIE - International Society for Optical Engineering, 2019. 1095022 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 10950).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Automatic detection and localization of bone erosion in hand HR-pQCT
AU - Ren, Jintao
AU - Arash Moaddel, H.
AU - Hauge, Ellen M.
AU - Keller, Kresten K.
AU - Jensen, Rasmus K.
AU - Lauze, François
PY - 2019
Y1 - 2019
N2 - Rheumatoid arthritis (RA) is an inflammatory disease which afflicts the joints with arthritis and periarticular bone destruction as a result. One of its central features is bone erosion, a consequence of excessive bone resorption and insufficient bone formation. High-resolution peripheral quantitative computed tomography (HR-pQCT) is a promising tool for monitoring RA. Quantification of bone erosions and detection of possible progression is essential in the management of treatment. Detection is performed manually and is a very demanding task as rheumatologists must annotate hundreds of 2D images and inspect any region of the bone structure that is suspected to be a sign of RA. We propose a 2D based method which combines an accurate segmentation of bone surface boundary and classification of patches along the surface as healthy or eroded. We use a series of classical image processing methods to segment CT volumes semi-automatically. They are used as training data for a U-Net. We train a Siamese net to learn the difference between healthy and eroded patches. The Siamese net alleviates the problem of highly imbalanced class labels by providing a base for one-shot learning of differences between patches. We trained and tested the method using 3 full HR-pQCT scans with bone erosion of various size. The proposed pipeline succeeded in classifying healthy and eroded patches with high precision and recall. The proposed algorithm is a preliminary work to demonstrate the potential of our pipeline in automating the process of detecting and locating the eroded regions of bone surfaces affected by RA.
AB - Rheumatoid arthritis (RA) is an inflammatory disease which afflicts the joints with arthritis and periarticular bone destruction as a result. One of its central features is bone erosion, a consequence of excessive bone resorption and insufficient bone formation. High-resolution peripheral quantitative computed tomography (HR-pQCT) is a promising tool for monitoring RA. Quantification of bone erosions and detection of possible progression is essential in the management of treatment. Detection is performed manually and is a very demanding task as rheumatologists must annotate hundreds of 2D images and inspect any region of the bone structure that is suspected to be a sign of RA. We propose a 2D based method which combines an accurate segmentation of bone surface boundary and classification of patches along the surface as healthy or eroded. We use a series of classical image processing methods to segment CT volumes semi-automatically. They are used as training data for a U-Net. We train a Siamese net to learn the difference between healthy and eroded patches. The Siamese net alleviates the problem of highly imbalanced class labels by providing a base for one-shot learning of differences between patches. We trained and tested the method using 3 full HR-pQCT scans with bone erosion of various size. The proposed pipeline succeeded in classifying healthy and eroded patches with high precision and recall. The proposed algorithm is a preliminary work to demonstrate the potential of our pipeline in automating the process of detecting and locating the eroded regions of bone surfaces affected by RA.
KW - Active Contours
KW - Bone Erosion
KW - HR-pQCT
KW - Rheumatoid Arthritis
KW - Siamese Nets
KW - U-Nets
UR - http://www.scopus.com/inward/record.url?scp=85068183068&partnerID=8YFLogxK
U2 - 10.1117/12.2512876
DO - 10.1117/12.2512876
M3 - Article in proceedings
AN - SCOPUS:85068183068
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2019
A2 - Mori, Kensaku
A2 - Hahn, Horst K.
PB - SPIE - International Society for Optical Engineering
T2 - Medical Imaging 2019: Computer-Aided Diagnosis
Y2 - 17 February 2019 through 20 February 2019
ER -
ID: 227331677