Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United StatesbMedical College of Georgia, Augusta, Georgia, United States.
Georgia Institute of Technology and Emory University, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States.
J Biomed Opt. 2017 Jun 1;22(6):60503. doi: 10.1117/1.JBO.22.6.060503.
Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.
外科癌症切除需要准确和及时地诊断癌症边缘，以实现成功的患者缓解。高光谱成像 (HSI) 已成为一种有用的非接触式技术，用于获取组织的光谱和光学性质。开发了一种卷积神经网络 (CNN) 分类器，用于使用 HSI 对切除的鳞状细胞癌、甲状腺癌和正常头颈部组织样本进行分类。CNN 分类通过专门从事头颈部癌症的病理学家的手动注释进行验证。对 50 名患者的初步结果表明，HSI 和深度学习技术有可能对头颈部患者的手术标本进行自动组织标记。
