Tissue classification using machine learning to aid in intraoperative registration: a pilot study

Brandon Chan; Jason Auyeung; John F. Rudan; Parvin Mousavi; Manuela Kunz

doi:10.1117/12.2513033

8 March 2019 Tissue classification using machine learning to aid in intraoperative registration: a pilot study

Brandon Chan, Jason Auyeung, John F. Rudan, Parvin Mousavi, Manuela Kunz

Proceedings Volume 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling; 1095136 (2019) https://doi.org/10.1117/12.2513033
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Modern handheld structured light scanners show potential in the field of medical imaging and image-guided surgery. For the effective use of such scanners as a rapid registration tool, anatomical regions of interest must be identified. The purpose of this study is to investigate the use of machine learning to classify various anatomical tissues using the textural information collected from structured light scanners. We performed an ex vitro study using three fresh frozen knee specimens. Each specimen underwent multiple stages of dissection to reveal different anatomical tissues. At each stage of dissection, the specimens were scanned with a structured light scanner (Artec Spider, Artec Group, Palo Alto, USA). Using the texture information of the scanned model, a domain expert manually segmented four tissues of interest: muscle, tendon, cartilage, and bone. The RGB and HSL values of the data points in the manually segmented models were extracted for use in training and evaluating a random forest classifier. Our trained random forest classifier obtained a four-class classification accuracy of 77% and a three-class classification accuracy of 82%. The results of this study demonstrate the feasibility of a random forest to aid in semi-automatic or automatic segmentation of anatomical tissues using only textural information. Further experiments with in vivo tissues will need to be done to validate the application of such classifiers in an intraoperative environment.

Citation Download Citation

Brandon Chan, Jason Auyeung, John F. Rudan, Parvin Mousavi, and Manuela Kunz "Tissue classification using machine learning to aid in intraoperative registration: a pilot study", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 1095136 (8 March 2019); https://doi.org/10.1117/12.2513033

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KEYWORDS

Tissues

Scanners

Cartilage

RGB color model

3D modeling

Structured light

Bone

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