Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging.
Automatic detection of head and neck squamous cell carcinoma on histologic slides using hyperspectral microscopic imaging.
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DOI：
10.1117/1.jbo.27.4.046501
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发表时间：
2022-04
影响因子：
3.5
通讯作者：
Fei, Baowei
中科院分区：
文献类型：
--
作者：
Ma, Ling;Little, James, V;Chen, Amy Y.;Myers, Larry;Sumer, Baran D.;Fei, Baowei
关键词：
自动、快速和准确地识别组织学载玻片上的癌症在肿瘤病理学中有许多应用。本研究的目的是探讨高光谱成像（HSI）的自动检测头颈部癌细胞核的组织切片，以及癌症区域识别的基础上核检测。一个定制的高光谱显微成像系统的开发和使用扫描组织切片从20例鳞状细胞癌（SCC）。获得并配准具有相同视野的组织学载玻片的高光谱图像和红色、绿色和蓝色（RGB）图像。提出了一种基于主成分分析的细胞核分割方法，从高光谱图像和配准后的RGB图像中提取细胞核块。基于光谱的支持向量机和基于块的卷积神经网络（CNN）实现细胞核分类。用分割的细胞核的RGB补丁（RGB-CNN）和高光谱补丁（HSI-CNN）训练CNN，并评估HSI提供的额外光谱信息的效用。此外，通过基于在每个图像中检测到的癌细胞核的百分比的逐图像分类来实现癌症区域识别。RGB-CNN主要利用细胞核的空间信息，验证精度为0.81，测试精度为0.74。HSI-CNN利用了细胞核的空间和光谱特征，在分类性能方面表现出了显着的改进，并达到了0.89的验证精度和0.82的测试精度。此外，基于细胞核检测的图像级癌症区域识别通常可以提高癌症检测率。我们证明了形态和光谱信息有助于SCC细胞核分化，并且高光谱图像内的光谱信息可以提高分类性能。
Automatic, fast, and accurate identification of cancer on histologic slides has many applications in oncologic pathology. The purpose of this study is to investigate hyperspectral imaging (HSI) for automatic detection of head and neck cancer nuclei in histologic slides, as well as cancer region identification based on nuclei detection. A customized hyperspectral microscopic imaging system was developed and used to scan histologic slides from 20 patients with squamous cell carcinoma (SCC). Hyperspectral images and red, green, and blue (RGB) images of the histologic slides with the same field of view were obtained and registered. A principal component analysis-based nuclei segmentation method was developed to extract nuclei patches from the hyperspectral images and the coregistered RGB images. Spectra-based support vector machine and patch-based convolutional neural networks (CNNs) were implemented for nuclei classification. The CNNs were trained with RGB patches (RGB-CNN) and hyperspectral patches (HSI-CNN) of the segmented nuclei and the utility of the extra spectral information provided by HSI was evaluated. Furthermore, cancer region identification was implemented by image-wise classification based on the percentage of cancerous nuclei detected in each image. RGB-CNN, which mainly used the spatial information of nuclei, resulted in a 0.81 validation accuracy and 0.74 testing accuracy. HSI-CNN, which utilized the spatial and spectral features of the nuclei, showed significant improvement in classification performance and achieved 0.89 validation accuracy as well as 0.82 testing accuracy. Furthermore, the image-wise cancer region identification based on nuclei detection could generally improve the cancer detection rate. We demonstrated that the morphological and spectral information contribute to SCC nuclei differentiation and that the spectral information within hyperspectral images could improve classification performance.