TY - GEN
T1 - Combining diffuse reflectance spectroscopy and ultrasound imaging for resection margin assessment during colorectal cancer surgery
AU - Geldof, Freija
AU - Dashtbozorg, Behdad
AU - Hendriks, Bernardus H. W.
AU - Sterenborg, Henricus J. C. M.
AU - Ruers, Theo J. M.
N1 - Funding Information: This work was supported by KFW-STW (15022) and Philips Research, Eindhoven, The Netherlands. We are grateful for their support. Publisher Copyright: © 2021 SPIE.
PY - 2021
Y1 - 2021
N2 - Establishing adequate resection margins during colorectal cancer surgery is challenging. Currently, in up to 30% of the cases the tumor is not completely removed, which emphasizes the lack of a real-time tissue discrimination tool that can assess resection margins up to multiple millimeters in depth. Therefore, we propose to combine spectral data from diffuse reflectance spectroscopy (DRS) with spatial information from ultrasound (US) imaging to evaluate multi-layered tissue structures. First, measurements with animal tissue were performed to evaluate the feasibility of the concept. The phantoms consisted of muscle and fat layers, with a varying top layer thickness of 0-10 mm. DRS spectra of 250 locations were obtained and corresponding US images were acquired. DRS features were extracted using the wavelet transform. US features were extracted based on the graph theory and first-order gradient. Using a regression analysis and combined DRS and US features, the top layer thickness was estimated with an error of up to 0.48 mm. The tissue types of the first and second layers were classified with accuracies of 0.95 and 0.99 respectively, using a support vector machine model.
AB - Establishing adequate resection margins during colorectal cancer surgery is challenging. Currently, in up to 30% of the cases the tumor is not completely removed, which emphasizes the lack of a real-time tissue discrimination tool that can assess resection margins up to multiple millimeters in depth. Therefore, we propose to combine spectral data from diffuse reflectance spectroscopy (DRS) with spatial information from ultrasound (US) imaging to evaluate multi-layered tissue structures. First, measurements with animal tissue were performed to evaluate the feasibility of the concept. The phantoms consisted of muscle and fat layers, with a varying top layer thickness of 0-10 mm. DRS spectra of 250 locations were obtained and corresponding US images were acquired. DRS features were extracted using the wavelet transform. US features were extracted based on the graph theory and first-order gradient. Using a regression analysis and combined DRS and US features, the top layer thickness was estimated with an error of up to 0.48 mm. The tissue types of the first and second layers were classified with accuracies of 0.95 and 0.99 respectively, using a support vector machine model.
KW - Colorectal cancer
KW - Diffuse reflectance spectroscopy
KW - Margin assessment
KW - Multi-layer tissue
KW - Multimodal imaging
KW - Surgical guidance
KW - Tissue classification
KW - Ultrasound imaging
UR - http://www.scopus.com/inward/record.url?scp=85103781665&partnerID=8YFLogxK
U2 - https://doi.org/10.1117/12.2578478
DO - https://doi.org/10.1117/12.2578478
M3 - Conference contribution
VL - 11634
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Multimodal Biomedical Imaging XVI
A2 - Azar, Fred S.
A2 - Intes, Xavier
A2 - Fang, Qianqian
PB - SPIE
T2 - Multimodal Biomedical Imaging XVI 2021
Y2 - 6 March 2021 through 11 March 2021
ER -