| Autor: |
Prashant Pandey, Benjamin Hohlmann, Peter Brößner, Ilker Hacihaliloglu, Keiran Barr, Tamas Ungi, Oliver Zettinig, Raphael Prevost, Guillaume Dardenne, Zian Fanti, Wolfgang Wein, Eric Stindel, Fernando Arambula Cosio, Pierre Guy, Gabor Fichtinger, Klaus Radermacher, Antony J. Hodgson |
| Rok vydání: |
2022 |
| Zdroj: |
EPiC Series in Health Sciences. |
| ISSN: |
2398-5305 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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