Deep learning techniques for bone surface delineation in ultrasound

Matija Ciganovic; Firat Özdemir; Mazda Farshad; Orcun Göksel

doi:10.1117/12.2512997

15 March 2019 Deep learning techniques for bone surface delineation in ultrasound

Matija Ciganovic, Firat Özdemir, Mazda Farshad, Orcun Göksel

Proceedings Volume 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography; 109550Y (2019) https://doi.org/10.1117/12.2512997
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

For computer-assisted interventions in orthopedic surgery, automatic bone surface delineation can be of great value. For instance, given such a method, an automatically extracted bone surface from intraoperative imaging modalities can be registered to the bone surfaces from preoperative images, allowing for enhanced visualization and/or surgical guidance. Ultrasound (US) is ideal for imaging bone surfaces intraoperatively, being real-time, non-ionizing, and cost-effective. However, due to its low signal-to-noise ratio and imaging artifacts, extracting bone surfaces automatically from such images remains challenging. In this work, we examine the suitability of deep learning for automatic bone surface extraction from US. Given 1800 manually annotated US frames, we examine the performance of two popular neural networks used for segmentation. Furthermore, we investigate the effect of different preprocessing methods used for manual annotations in training on the final segmentation quality, and demonstrate excellent qualitative and quantitative segmentation results.

Citation Download Citation

Matija Ciganovic, Firat Özdemir, Mazda Farshad, and Orcun Göksel "Deep learning techniques for bone surface delineation in ultrasound", Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 109550Y (15 March 2019); https://doi.org/10.1117/12.2512997

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