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A technique for semiautomatic segmentation of echogenic structures in 3D ultrasound, applied to infant hip dysplasia

  • Abhilash Rakkunedeth HareendranathanEmail author
  • Myles Mabee
  • Kumaradevan Punithakumar
  • Michelle Noga
  • Jacob L. Jaremko
Original Article

Abstract

Purpose

Automatic segmentation of anatomical structures and lesions from medical ultrasound images is a formidable challenge in medical imaging due to image noise, blur and artifacts. In this paper we present a segmentation technique with features highly suited to use in noisy 3D ultrasound volumes and demonstrate its use in modeling bone, specifically the acetabulum in infant hips. Quantification of the acetabular shape is crucial in diagnosing developmental dysplasia of the hip (DDH), a common condition associated with hip dislocation and premature osteoarthritis if not treated. The well-established Graf technique for DDH diagnosis has been criticized for high inter-observer and inter-scan variability. In our earlier work we have introduced a more reliable instability metric based on 3D ultrasound data. Visualizing and interpreting the acetabular shape from noisy 3D ultrasound volumes has been one of the major roadblocks in using 3D ultrasound as diagnostic tool for DDH. For this study we developed a semiautomated segmentation technique to rapidly generate 3D acetabular surface models and classified the acetabulum based on acetabular contact angle (ACA) derived from the models. We tested the feasibility and reliability of the technique compared with manual segmentation.

Methods

The proposed segmentation algorithm is based on graph search. We formulate segmentation of the acetabulum as an optimal path finding problem on an undirected weighted graph. Slice contours are defined as the optimal path passing through a set of user-defined seed points in the graph, and it can be found using dynamic programming techniques (in this case Dijkstra’s algorithm). Slice contours are then interpolated over the 3D volume to generate the surface model. A three-dimensional ACA was calculated using normal vectors of the surface model.

Results

The algorithm was tested over an extensive dataset of 51 infant ultrasound hip volumes obtained from 42 subjects with normal to dysplastic hips. The contours generated by the segmentation algorithm closely matched with those obtained from manual segmentation. The average RMS errors between the semiautomated and manual segmentation for the 51 volumes were 0.28 mm/1.1 voxel (with 2 node points) and 0.24 mm/0.9 voxel (with 3 node points). The semiautomatic algorithm gave visually acceptable results on images with moderate levels of noise and was able to trace the boundary of the acetabulum even in the presence of significant shadowing. Semiautomatic contouring was also faster than manual segmentation at 37 versus 56 s per scan. It also improved the repeatability of the ACA calculation with inter-observer and intra-observer variability of \(1.4 \pm 0.9\) degree and \(1.4 \pm 1.0\) degree.

Conclusion

The semiautomatic segmentation technique proposed in this work offers a fast and reliable method to delineate the contours of the acetabulum from 3D ultrasound volumes of the hip. Since the technique does not rely upon contour evolution, it is less susceptible than other methods to the frequent missing or incomplete boundaries and noise artifacts common in ultrasound images. ACA derived from the segmented 3D surface was able to accurately classify the acetabulum under the categories normal, borderline and dysplastic. The semiautomatic technique makes it easier to segment the volume and reduces the inter-observer and intra-observer variation in ACA calculation compared with manual segmentation. The method can be applied to any structure with an echogenic boundary on ultrasound (such as a ventricle, blood vessel, organ or tumor), or even to structures with a bright border on computed tomography or magnetic resonance imaging.

Keywords

Semiautomatic segmentation Developmental dysplasia of the hip (DDH) Graph-search-based segmentation 3D Musculoskeletal ultrasound 

Notes

Acknowledgments

The authors wish to thank Servier Canada, Radiologic Society of North America (RSNA) Research Seed Grant and CIHR Institute of Human Development, Child and Youth Health (IHDCYH), Grant NI15-004 for the research funding which supported this work. We also thank Dr Pierre Boulanger (Scientific Director, Servier Virtual Cardiac Centre, University of Alberta) for his insight and expertise that greatly helped in this research.

Conflict of interest

The authors Abhilash Rakkunedeth Hareendranathan, Myles Mabee, Kumaradevan Punithakumar, Michelle Noga and Jacob L. Jaremko declare that they have no conflict of interest.

Ethical standard

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).

Informed consent

Informed consent was obtained from all patients for being included in the study.

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Copyright information

© CARS 2015

Authors and Affiliations

  • Abhilash Rakkunedeth Hareendranathan
    • 1
    Email author
  • Myles Mabee
    • 1
  • Kumaradevan Punithakumar
    • 1
  • Michelle Noga
    • 1
  • Jacob L. Jaremko
    • 1
  1. 1.2A2.42 Walter Mackenzie Health Sciences Centre, Department of Radiology and Diagnostic ImagingUniversity of AlbertaEdmontonCanada

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