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Shadow Peak: Accurate Real-time Bone Segmentation for Ultrasound and Developmental Dysplasia of the Hip

5 pagesPublished: October 26, 2019

Abstract

Confidence-weighted structured phase symmetry (CSPS) is a state-of-the-art bone seg- mentation technique for ultrasound (US), which has been recently proposed for automatic diagnosis for developmental dysplasia of the hip (DDH). However, CSPS relies on complex image phase feature analysis which is computationally expensive, and in our preliminary tests we have found it to be sometimes inaccurate. We evaluate a simpler alternative segmentation technique which we previously published, called Shadow Peak (SP), which uses intensity analysis to perform fast and accurate US bone segmentation. On average, SP segmentation ran 15 times faster for 2D US images, when tested on 15 hip images of pediatric patients. Furthermore, SP improves the segmentation F-score to 94%, compared to 72% when using CSPS segmentation.

Keyphrases: bone segmentation, computer assisted orthopaedic surgery, developmental dysplasia of the hip, ultrasound

In: Patrick Meere and Ferdinando Rodriguez Y Baena (editors). CAOS 2019. The 19th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 3, pages 301-305.

BibTeX entry
@inproceedings{CAOS2019:Shadow_Peak_Accurate_Real,
  author    = {Prashant Pandey and Niamul Quader and Kishore Mulpuri and Pierre Guy and Rafeef Garbi and Antony J. Hodgson},
  title     = {Shadow Peak: Accurate Real-time Bone Segmentation for Ultrasound and Developmental Dysplasia of the Hip},
  booktitle = {CAOS 2019. The 19th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Patrick Meere and Ferdinando Rodriguez Y Baena},
  series    = {EPiC Series in Health Sciences},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {/publications/paper/kK6h},
  doi       = {10.29007/w172},
  pages     = {301-305},
  year      = {2019}}
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