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Standardized Evaluation of Current Ultrasound Bone Segmentation Algorithms on Multiple Datasets

6 pagesPublished: December 13, 2022

Abstract

Ultrasound (US) bone segmentation is an important component of US-guided or- thopaedic procedures. While there are many published segmentation techniques, there is no direct way to compare their performance. We present a solution to this, by curating a multi-institutional set of US images and corresponding segmentations, and systematically evaluating six previously-published bone segmentation algorithms using consistent metric definitions. We find that learning-based segmentation methods outperform traditional al- gorithms that rely on hand-crafted image features, as measured by their Dice scores, RMS distance errors and segmentation success rates. However, there is no single best performing algorithm across the datasets, emphasizing the need for carefully evaluating techniques on large, heterogenous datasets. The datasets and evaluation framework described can be used to accelerate development of new segmentation algorithms.

Keyphrases: Bone, challenge, Computer-assisted orthopaedic surgery, Orthopaedics, Segmentation, Ultrasound

In: Ferdinando Rodriguez Y Baena, Joshua W Giles and Eric Stindel (editors). Proceedings of The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 5, pages 148--153

Links:
BibTeX entry
@inproceedings{CAOS2022:Standardized_Evaluation_of_Current,
  author    = {Prashant Pandey and Benjamin Hohlmann and Peter Br\textbackslash{}"o\{\textbackslash{}ss\}ner and Ilker Hacihaliloglu and Keiran Barr and Tamas Ungi and Oliver Zettinig and Raphael Prevost and Guillaume Dardenne and Zian Fanti and Wolfgang Wein and Eric Stindel and Fernando Arambula Cosio and Pierre Guy and Gabor Fichtinger and Klaus Radermacher and Antony J. Hodgson},
  title     = {Standardized Evaluation of Current Ultrasound Bone Segmentation Algorithms on Multiple Datasets},
  booktitle = {Proceedings of The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery},
  editor    = {Ferdinando Rodriguez Y Baena and Joshua W Giles and Eric Stindel},
  series    = {EPiC Series in Health Sciences},
  volume    = {5},
  pages     = {148--153},
  year      = {2022},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-5305},
  url       = {https://easychair.org/publications/paper/4mhjf},
  doi       = {10.29007/q51n}}
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