用于术中像素级分类肝转移性结肠癌的高光谱成像数据集与方法学
A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver
AI总结：
针对术中高光谱成像(HSI)数据缺乏的问题，构建了结肠癌肝转移冷冻切片HSI数据集，并提出SSL-MPRI方法，仅用1%标注像素即优于六种深度学习模型，显著提升了像素级分类性能。
摘要：
高光谱成像（HSI）在变革计算病理学领域方面具有巨大潜力。然而，目前缺乏训练深度学习（DL）模型所需的像素级标注HSI数据。此外，基于HSI的研究数量仍然有限，且在许多情况下，HSI相对于传统RGB成像的优势尚未得到确凿证明，特别是对于术中采集的样本。为了应对这些挑战，我们提出了一个包含27张HSI图像的数据库，这些图像为苏木精-伊红染色的冷冻切片，采集自14名结肠腺癌肝转移患者。该数据库旨在验证术中肿瘤切除术的像素级分类。HSI的采集光谱范围为450至800 nm，分辨率为1 nm，生成的图像尺寸为1384x1035像素。像素级标注由三位病理学家完成。为了克服实验变异性和缺乏标注数据等挑战，我们将基于标签传播的半监督学习（SSL）与通过以下方法提取的光谱-空间特征相结合：相关信息多尺度原理（MPRI）方法和张量奇异谱分析方法。在每类仅使用1%标注像素的情况下，SSL-MPRI方法在HSI数据集上实现了0.9313的微观平衡准确率（BACC）和0.9235的微观F1分数。在相应的RGB图像上的性能较低，微观BACC为0.8809，微观F1分数为0.8688。这些改进具有统计学显著性。SSL-MPRI方法优于使用63%标注像素训练的六种DL架构。数据和代码可在以下网址获取：https://github.com/ikopriva/ColonCancerHSI。
英文摘要：
Hyperspectral imaging (HSI) holds significant potential for transforming the field of computational pathology. However, there is currently a shortage of pixel-wise annotated HSI data necessary for training deep learning (DL) models. Additionally, the number of HSI-based research studies remains limited, and in many cases, the advantages of HSI over traditional RGB imaging have not been conclusively demonstrated, particularly for specimens collected intraoperatively. To address these challenges we present a database consisted of 27 HSIs of hematoxylin-eosin stained frozen sections, collected from 14 patients with colon adenocarcinoma metastasized to the liver. It is aimed to validate pixel-wise classification for intraoperative tumor resection. The HSIs were acquired in the spectral range of 450 to 800 nm, with a resolution of 1 nm, resulting in images of 1384x1035 pixels. Pixel-wise annotations were performed by three pathologists. To overcome challenges such as experimental variability and the lack of annotated data, we combined label-propagation-based semi-supervised learning (SSL) with spectral-spatial features extracted by: the multiscale principle of relevant information (MPRI) method and tensor singular spectrum analysis method. Using only 1% of labeled pixels per class the SSL-MPRI method achieved a micro balanced accuracy (BACC) of 0.9313 and a micro F1-score of 0.9235 on the HSI dataset. The performance on corresponding RGB images was lower, with a micro BACC of 0.8809 and a micro F1-score of 0.8688. These improvements are statistically significant. The SSL-MPRI approach outperformed six DL architectures trained with 63% of labeled pixels. Data and code are available at: https://github.com/ikopriva/ColonCancerHSI.
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