基于数据增强的高光谱细胞图像中癌细胞质分割
Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation
AI总结：
本文针对高光谱细胞图像中癌细胞质分割问题，提出一种利用CMOS图像进行数据增强以应对仪器噪声和标注困难的方法，并通过实验验证了其有效性。
摘要：
苏木精-伊红（H&E）染色图像通常用于从显微镜拍摄的图像中检测细胞核或癌变区域。识别癌细胞质对于确定癌症类型至关重要；因此，在细胞图像中获取准确的癌细胞质区域非常重要。虽然CMOS图像往往缺乏诊断所需的详细信息，但高光谱图像能提供更全面的细胞信息。我们提出一种利用深度学习模型在高光谱图像中检测癌细胞质的方法。深度学习模型需要大规模数据集进行学习；然而，获取大量高光谱图像十分困难。此外，高光谱图像经常包含仪器噪声，这取决于成像设备的特性。我们提出了一种考虑仪器噪声的数据增强方法。由于CMOS图像视觉清晰度高，相比原始高光谱图像更便于人工标注，因此使用CMOS图像进行数据增强。实验结果表明，所提出的数据增强方法在定量和定性两方面均具有有效性。
英文摘要：
Hematoxylin and Eosin (H&E)-stained images are commonly used to detect nuclear or cancerous regions in cells from images captured by a microscope. Identifying cancer cytoplasm is crucial for determining the type of cancer; hence, obtaining accurate cancer cytoplasm regions in cell images is important. While CMOS images often lack detailed information necessary for diagnosis, hyperspectral images provide more comprehensive cell information. Using a deep learning model, we propose a method for detecting cancer cell cytoplasm in hyperspectral images. Deep learning models require large datasets for learning; however, capturing a large number of hyperspectral images is difficult. Additionally, hyperspectral images frequently contain instrumental noise, depending on the characteristics of the imaging devices. We propose a data augmentation method to account for instrumental noise. CMOS images were used for data augmentation owing to their visual clarity, which facilitates manual annotation compared to original hyperspectral images. Experimental results demonstrate the effectiveness of the proposed data augmentation method both quantitatively and qualitatively.
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