Tran Minh Ha, Ma Ling, Litter James V, Chen Amy Y, Fei Baowei
Univ. of Texas at Dallas, Dept. of Bioengineering, Richardson, TX.
Univ. of Texas at Dallas, Center for Imaging and Surgical Innovation, Richardson, TX.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12039. doi: 10.1117/12.2612963. Epub 2022 Apr 4.
Hyperspectral imaging (HSI), a non-invasive imaging modality, has been successfully used in many different biological and medical applications. One such application is in the field of oncology, where hyperspectral imaging is being used on histologic samples. This study compares the performances of different image classifiers using different imaging modalities as training data. From a database of 33 fixed tissues from head and neck patients with follicular thyroid carcinoma, we produced three different datasets: an RGB image dataset that was acquired from a whole slide image scanner, a hyperspectral (HS) dataset that was acquired with a compact hyperspectral camera, and an HS-synthesized RGB image dataset. Three separate deep learning classifiers were trained using the three datasets. We show that the deep learning classifier trained on HSI data has an area under the receiver operator characteristic curve (AUC-ROC) of 0.966, higher than that of the classifiers trained on RGB and HSI-synthesized RGB data. This study demonstrates that hyperspectral images improve the performance of cancer classification on whole histologic slides. Hyperspectral imaging and deep learning provide an automatic tool for thyroid cancer detection on whole histologic slides.
高光谱成像（HSI）是一种非侵入性成像方式，已成功应用于许多不同的生物和医学领域。其中一个应用领域是肿瘤学，高光谱成像正用于组织学样本。本研究比较了使用不同成像方式作为训练数据的不同图像分类器的性能。从33例患有滤泡性甲状腺癌的头颈患者的固定组织数据库中，我们生成了三个不同的数据集：一个从全切片图像扫描仪获取的RGB图像数据集、一个用紧凑型高光谱相机获取的高光谱（HS）数据集以及一个HS合成RGB图像数据集。使用这三个数据集分别训练了三个深度学习分类器。我们表明，在HSI数据上训练的深度学习分类器的受试者工作特征曲线下面积（AUC-ROC）为0.966，高于在RGB和HS合成RGB数据上训练的分类器。本研究表明，高光谱图像提高了全组织学切片上癌症分类的性能。高光谱成像和深度学习为全组织学切片上的甲状腺癌检测提供了一种自动工具。
