for: This paper aims to provide a high-content stimulated Raman histology (HC-SRH) platform for cancer diagnosis based on un-stained breast tissues, which can provide both morphological and chemical information.
methods: The HC-SRH platform uses spectral unmixing in the C-H vibration window to map unsaturated lipids, cellular protein, extracellular matrix, saturated lipid, and water in breast tissue, and spectral selective sampling is implemented to boost the speed of HC-SRH.
results: The HC-SRH platform provides excellent contrast for various tissue components, and the advanced fiber laser-based SRS microscopy demonstrates the HC-SRH in a clinical-compatible manner, showing a clear chemical contrast of nucleic acid and solid-state ester in the fingerprint result.Abstract
Histological examination is crucial for cancer diagnosis, including hematoxylin and eosin (H&E) staining for mapping morphology and immunohistochemistry (IHC) staining for revealing chemical information. Recently developed two-color stimulated Raman histology could bypass the complex tissue processing to mimic H&E-like morphology. Yet, the underlying chemical features are not revealed, compromising the effectiveness of prognostic stratification. Here, we present a high-content stimulated Raman histology (HC-SRH) platform that provides both morphological and chemical information for cancer diagnosis based on un-stained breast tissues. Through spectral unmixing in the C-H vibration window, HC-SRH can map unsaturated lipids, cellular protein, extracellular matrix, saturated lipid, and water in breast tissue. In this way, HC-SRH provides excellent contrast for various tissue components. Considering rapidness is important in clinical trials, we implemented spectral selective sampling to boost the speed of HC-SRH by one order. We also successfully demonstrated the HC-SRH in a clinical-compatible fiber laser-based SRS microscopy. With the widely rapid tuning capability of the advanced fiber laser, a clear chemical contrast of nucleic acid and solid-state ester is shown in the fingerprint result.
摘要
histological 检查是癌病诊断中不可或缺的，包括杂谱和染色技术。 latest developments in two-color stimulated Raman histology can mimic H&E-like morphology, but the underlying chemical features are not revealed, which compromises the effectiveness of prognostic stratification. Here, we present a high-content stimulated Raman histology (HC-SRH) platform that provides both morphological and chemical information for cancer diagnosis based on un-stained breast tissues. Through spectral unmixing in the C-H vibration window, HC-SRH can map unsaturated lipids, cellular protein, extracellular matrix, saturated lipid, and water in breast tissue. In this way, HC-SRH provides excellent contrast for various tissue components. Considering rapidness is important in clinical trials, we implemented spectral selective sampling to boost the speed of HC-SRH by one order. We also successfully demonstrated the HC-SRH in a clinical-compatible fiber laser-based SRS microscopy. With the widely rapid tuning capability of the advanced fiber laser, a clear chemical contrast of nucleic acid and solid-state ester is shown in the fingerprint result.
Lightning-Fast Dual-Layer Lossless Coding for Radiance Format High Dynamic Range Images
results: 对比现有方法，该编码方法可以减少平均比特率约为1.57%-6.68%，并且显著减少解码器实现时间约为87.13%-98.96%。Abstract
This paper proposes a fast dual-layer lossless coding for high dynamic range images (HDRIs) in the Radiance format. The coding, which consists of a base layer and a lossless enhancement layer, provides a standard dynamic range image (SDRI) without requiring an additional algorithm at the decoder and can losslessly decode the HDRI by adding the residual signals (residuals) between the HDRI and SDRI to the SDRI, if desired. To suppress the dynamic range of the residuals in the enhancement layer, the coding directly uses the mantissa and exponent information from the Radiance format. To further reduce the residual energy, each mantissa is modeled (estimated) as a linear function, i.e., a simple linear regression, of the encoded-decoded SDRI in each region with the same exponent. This is called simple linear regressive mantissa estimator. Experimental results show that, compared with existing methods, our coding reduces the average bitrate by approximately $1.57$-$6.68$ % and significantly reduces the average encoder implementation time by approximately $87.13$-$98.96$ %.
摘要
这篇论文提出了一种快速双层无损编码器，用于高动态范围图像（HDRIs）在辉度格式下。该编码器由基层和无损增强层组成，可以将标准动态范围图像（SDRI）转换为HDRIs，无需额外算法在解码器端。此外，该编码器还可以losslessly解码HDRIs，只需将差异信号（差异）加到SDRI上即可。为了减少增强层的动态范围，该编码器直接使用Radiance格式中的杠志和指数信息。进一步减少差异能量，每个杠志都被模型为在每个区域中的线性函数，即简单的线性回归。这被称为简单的线性回归杠志估计器。实验结果表明，相比现有方法，我们的编码器可以将平均比特率降低约1.57%-6.68%，并显著降低解码器实现时间约87.13%-98.96%。