Zhou Ximing, Ma Ling, Mubarak Hasan K, Little James V, Chen Amy Y, Myers Larry L, Sumer Baran D, Fei Baowei
The University of Texas at Dallas, Department of Bioengineering, Richardson, TX.
Emory University, Department of Pathology and Laboratory Medicine, Atlanta, GA.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12039. doi: 10.1117/12.2614624. Epub 2022 Apr 4.
The study is to incorporate polarized hyperspectral imaging (PHSI) with deep learning for automatic detection of head and neck squamous cell carcinoma (SCC) on hematoxylin and eosin (H&E) stained tissue slides. A polarized hyperspectral imaging microscope had been developed in our group. In this paper, we firstly collected the Stokes vector data cubes (S0, S1, S2, and S3) of histologic slides from 17 patients with SCC by the PHSI microscope, under the wavelength range from 467 nm to 750 nm. Secondly, we generated the synthetic RGB images from the original Stokes vector data cubes. Thirdly, we cropped the synthetic RGB images into image patches at the image size of 96×96 pixels, and then set up a ResNet50-based convolutional neural network (CNN) to classify the image patches of the four Stokes vector parameters (S0, S1, S2, and S3) by application of transfer learning. To test the performances of the model, each time we trained the model based on the image patches (S0, S1, S2, and S3) of 16 patients out of 17 patients, and used the trained model to calculate the testing accuracy based on the image patches of the rest 1 patient (S0, S1, S2, and S3). We repeated the process for 6 times and obtained 24 testing accuracies (S0, S1, S2, and S3) from 6 different patients out of the 17 patients. The preliminary results showed that the average testing accuracy (84.2%) on S3 outperformed the average testing accuracy (83.5%) on S0. Furthermore, 4 of 6 testing accuracies of S3 (96.0%, 87.3%, 82.8%, and 86.7%) outperformed the testing accuracies of S0 (93.3%, 85.2%, 80.2%, and 79.0%). The study demonstrated the potential of using polarized hyperspectral imaging and deep learning for automatic detection of head and neck SCC on pathologic slides.
本研究旨在将偏振高光谱成像（PHSI）与深度学习相结合，用于在苏木精和伊红（H&E）染色的组织切片上自动检测头颈部鳞状细胞癌（SCC）。我们团队已开发出一种偏振高光谱成像显微镜。在本文中，我们首先通过PHSI显微镜在467纳米至750纳米的波长范围内，收集了17例SCC患者组织切片的斯托克斯矢量数据立方体（S0、S1、S2和S3）。其次，我们从原始斯托克斯矢量数据立方体生成合成RGB图像。第三，我们将合成RGB图像裁剪为96×96像素大小的图像块，然后通过迁移学习建立基于ResNet50的卷积神经网络（CNN），对四个斯托克斯矢量参数（S0、S1、S2和S3）的图像块进行分类。为了测试模型的性能，每次我们基于17例患者中16例患者的图像块（S0、S1、S2和S3）训练模型，并使用训练好的模型根据其余1例患者的图像块（S0、S1、S2和S3）计算测试准确率。我们重复这个过程6次，从17例患者中的6例不同患者那里获得了24个测试准确率（S0、S1、S2和S3）。初步结果表明，S3的平均测试准确率（84.2%）优于S0的平均测试准确率（83.5%）。此外，S3的6个测试准确率中有4个（96.0%、87.3%、82.8%和86.7%）优于S0的测试准确率（93.3%、85.2%、80.2%和79.0%）。该研究证明了使用偏振高光谱成像和深度学习在病理切片上自动检测头颈部SCC的潜力。
