Publication Info

• Created By
  P. Pandey
  B. Hohlmann
  P. Broessner
  I. Hacihaliloglu
  K. Barr
  T. Ungi
  O. Zettinig
  R. Prevost
  G. Dardenne
  Z. Fanti
  W. Wein
  E. Stindel
  E. Stindel
  P. Guy
  G. Fichtinger
  K. Radermacher
  A. Hodgson
• Conference
  CAOS
• Publisher
  EPiC Series in Health Sciences
• Date
  2022
• Link
  https://doi.org/10.29007/q51n
• CAOS 2022 – Systematic evalaution of US Bone Segmentation

Project Description

Ultrasound (US) bone segmentation is an important component of US-guided orthopaedicprocedures. 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 algorithms 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.