KIDS HIPS

BoneFinder® is now also applied to fully automatically outline and segment the hip joint in radiographs of children's hips.

What are the aims of this research?

The goal is to develop a software system capable of helping clinicians interpret hip radiographs of children, focusing on the four most common diseases (Legg-Calve-Perthes disease, developmental dysplasia of the hip, slipped capital femoral epiphysis, and hip migration in cerebral palsy). These account for over 95% of cases of paediatric hip pathology. The system will aid the early detection of disease, and perform routine measurements usually conducted by surgeons to monitor the progression of disease.


Why is this research important?

The hip is the most common joint to be affected by disease in children, with radiographs taken to diagnose, monitor, and dictate treatment options. Hip deformities are painful disorders that affect 1 in 500 children. Children with hip deformities are more likely to develop osteoarthritis and often need hip replacement surgeries as young adults. In some cases, this hip replacement surgery could be avoided by treatment in childhood.


How will the findings benefit patients?

This work will help researchers to get a better understanding of childhood diseases affecting the hip. It aims to create better ways to accurately measure the hip and the severity of diseases affecting the hip joint in children. These measurements can be used to help predict future outcomes and allow doctors to select the best treatment for each child, reducing long-term pain and disability while also ensuring that similar standards are applied across different hospitals.

Perthes disease

Hi dysplasia

Slipped epiphysis

Cerebral palsy hip migration

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RESULTS

We performed fully automatic shape and appearance analyses to classify the proximal femur in hip radiographs of children into Legg-Calvé-Perthes (Perthes) disease and healthy. We trained and tested a BoneFinder® module to automatically outline the proximal femur using 58 points based on 1179 images. The BoneFinder® module was then applied to 601 unseen images (284 healthy and 317 Perthes). The automatically identified point positions were used to build statistical shape and appearance models and to evaluate their classification performance. Point placement performance is reported as mean point-to-curve error (where "mean" refers to averaging the errors over all points per image). Classification performance is reported as the area under the receiver operating characteristic curve (AUC).

Segmentation example showing the outline of a children's proximal femur. Our BoneFinder® module achieved a point-to-curve-error of less than 4% of the femoral shaft width for 95% of all 1179 images.

The fully automatic Perthes classification system was able to distinguish between Perthes and healthy childrens' hips with an AUC of 0.98 (SD:±0.01).


Data shown on this page is copyright (c) 2019 Springer Nature Switzerland AG (DOI: 10.1007/978-3-030-11166-3_8).

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PUBLICATIONS

  • Open Access
    (abstract)
    Detecting Perthes Disease and Investigating the Effects of Aging on Hip Shape in Children
    A.K. Davison, T.F. Cootes, D.C. Perry, W. Luo, Medical Student Annotation Collaborative and C. Lindner. Bone Research Society Annual Meeting 2020. Journal of Bone & Mineral Research Plus, Vol. 5, Art. Nr. e10499, page 63, 2021. DOI: 10.1002/jbm4.10499 (Best Clinical Poster Prize) [Virtual Poster]
  • Open Access
    (accepted author version)
    Landmark Localisation in Radiographs Using Weighted Heatmap Displacement Voting
    A.K. Davison, C. Lindner, D.C. Perry, W. Luo, Medical Student Annotation Collaborative and T.F. Cootes. Proceedings of the 6th MICCAI Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging (MSKI 2018), Granada, Spain. Springer-Verlag Lecture Notes in Computer Science 11404, pages 73-85, 2018. DOI: 10.1007/978-3-030-11166-3_7
  • Open Access
    (accepted author version)
    Perthes Disease Classification Using Shape and Appearance Modelling
    A.K. Davison, T.F. Cootes, D.C. Perry, W. Luo, Medical Student Annotation Collaborative and C. Lindner. Proceedings of the 6th MICCAI Workshop on Computational Methods and Clinical Applications in Musculoskeletal Imaging (MSKI 2018), Granada, Spain. Springer-Verlag Lecture Notes in Computer Science 11404, pages 86-98, 2018. DOI: 10.1007/978-3-030-11166-3_8

CONTACT

If you are interested in BoneFinder® and its capabilities please do not hesitate to get in touch by emailing claudia.lindner@manchester.ac.uk
www.manchester.ac.uk/research/claudia.lindner