@inproceedings{c41401d6371d4c4e8f76a720d7a60cb3,
title = "Pixel-level tumor margin assessment of surgical specimen with hyperspectral imaging and deep learning classification",
abstract = "Surgery is a major treatment method for squamous cell carcinoma (SCC). During surgery, insufficient tumor margin may lead to local recurrence of cancer. Hyperspectral imaging (HSI) is a promising optical imaging technique for in vivo cancer detection and tumor margin assessment. In this study, a fully convolutional network (FCN) was implemented for tumor detection and margin assessment in hyperspectral images of SCC. The FCN was trained and tested with hyperspectral images of 25 ex vivo SCC surgical specimens from 20 different patients. The network was evaluated per patient and achieved pixel-level tissue classification with an average AUC of 0.88, 0.83 accuracy, 0.84 sensitivity and 0.70 specificity. The 95% Hausdorff distance of assessed tumor margin in 17 patients was less than 2 mm, and the classification time of each tissue specimen took less than 10 seconds. The proposed method potentially facilitates intraoperative tumor margin assessment and improves surgical outcomes.",
keywords = "Classification, Hyperspectral imaging, Squamous cell carcinoma, Tumor margin assessment, U-Net",
author = "Ling Ma and Maysam Shahedi and Ted Shi and Martin Halicek and Little, {James V.} and Chen, {Amy Y.} and Myers, {Larry L.} and Sumer, {Baran D.} and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2581046",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Linte, {Cristian A.} and Siewerdsen, {Jeffrey H.}",
booktitle = "Medical Imaging 2021",
}