Evaluation metrics for bone segmentation in ultrasound

Matthew Lougheed; Gabor Fichtinger; Tamas Ungi

doi:10.1117/12.2081638

18 March 2015 Evaluation metrics for bone segmentation in ultrasound

Matthew Lougheed, Gabor Fichtinger, Tamas Ungi

Proceedings Volume 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling; 941516 (2015) https://doi.org/10.1117/12.2081638
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Tracked ultrasound is a safe alternative to X-ray for imaging bones. The interpretation of bony structures is challenging as ultrasound has no specific intensity characteristic of bones. Several image segmentation algorithms have been devised to identify bony structures. We propose an open-source framework that would aid in the development and comparison of such algorithms by quantitatively measuring segmentation performance in the ultrasound images. True-positive and false-negative metrics used in the framework quantify algorithm performance based on correctly segmented bone and correctly segmented boneless regions. Ground-truth for these metrics are defined manually and along with the corresponding automatically segmented image are used for the performance analysis. Manually created ground truth tests were generated to verify the accuracy of the analysis. Further evaluation metrics for determining average performance per slide and standard deviation are considered. The metrics provide a means of evaluating accuracy of frames along the length of a volume. This would aid in assessing the accuracy of the volume itself and the approach to image acquisition (positioning and frequency of frame). The framework was implemented as an open-source module of the 3D Slicer platform. The ground truth tests verified that the framework correctly calculates the implemented metrics. The developed framework provides a convenient way to evaluate bone segmentation algorithms. The implementation fits in a widely used application for segmentation algorithm prototyping. Future algorithm development will benefit by monitoring the effects of adjustments to an algorithm in a standard evaluation framework.

Citation Download Citation

Matthew Lougheed, Gabor Fichtinger, and Tamas Ungi "Evaluation metrics for bone segmentation in ultrasound", Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 941516 (18 March 2015); https://doi.org/10.1117/12.2081638

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KEYWORDS

Image segmentation

Bone

Ultrasonography

Algorithm development

Image processing algorithms and systems

Detection and tracking algorithms

3D acquisition

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