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22 February 2019 Automatic identification of metastatic lymph nodes in OCT images
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Abstract
Lymphatic metastasis is a main pathway of dissemination of malignancies. The diagnosis of metastasis in lymph nodes can help stage cancer or help the surgeons make intraoperative decisions. In addition, lymph nodes are more easily confused with other neck tissues during thyroid surgery. Therefore, identification of lymph nodes is very important. Up to now, the gold standard for identification of metastatic lymph nodes is still histological examination, which can only be performed ex vivo and needs a long time. Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technology that is capable of detecting microstructures in bio-tissues in real time. In this study, we demonstrated a method to identify metastatic lymph nodes automatically by intraoperative OCT imaging. With a home-made swept source OCT system, we obtained OCT images of different resected neck tissues, including lymph nodes with and without metastasis, thyroid, parathyroid, fat and muscle, from 28 patients undertaking thyroidectomy. The automatic identification algorithm was based on texture analysis and back-propagation artificial neural network (BP-ANN). 66 texture features of OCT images were extracted and 14 were selected and used for automatic identification experiments. The trained BP-ANN has an excellent performance in identifying OCT images of lymph nodes with the sensitivity of 98.9 % and specificity of 98.8 %. The accuracy of lymphatic metastasis diagnosis is 90.1 %.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fang Hou, Zihan Yang, Wenqing Gu, Yang Yu, and Yanmei Liang "Automatic identification of metastatic lymph nodes in OCT images", Proc. SPIE 10867, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIII, 108673G (22 February 2019); https://doi.org/10.1117/12.2511588
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Cited by 3 scholarly publications.
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KEYWORDS
Lymphatic system

Optical coherence tomography

Tissues

Neck

Cancer

Surgery

Image processing algorithms and systems

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