Samovar, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France.
TRIBVN/T-Life, 92800 Puteaux, France.
Sensors (Basel). 2023 Sep 16;23(18):7932. doi: 10.3390/s23187932.
The diagnosis of many diseases relies, at least on first intention, on an analysis of blood smears acquired with a microscope. However, image quality is often insufficient for the automation of such processing. A promising improvement concerns the acquisition of enriched information on samples. In particular, Quantitative Phase Imaging (QPI) techniques, which allow the digitization of the phase in complement to the intensity, are attracting growing interest. Such imaging allows the exploration of transparent objects not visible in the intensity image using the phase image only. Another direction proposes using stained images to reveal some characteristics of the cells in the intensity image; in this case, the phase information is not exploited. In this paper, we question the interest of using the bi-modal information brought by intensity and phase in a QPI acquisition when the samples are stained. We consider the problem of detecting parasitized red blood cells for diagnosing malaria from stained blood smears using a Deep Neural Network (DNN). Fourier Ptychographic Microscopy (FPM) is used as the computational microscopy framework to produce QPI images. We show that the bi-modal information enhances the detection performance by 4% compared to the intensity image only when the convolution in the DNN is implemented through a complex-based formalism. This proves that the DNN can benefit from the bi-modal enhanced information. We conjecture that these results should extend to other applications processed through QPI acquisition.
许多疾病的诊断至少在最初阶段依赖于使用显微镜获取的血液涂片分析。然而，图像质量通常不足以实现此类处理的自动化。一种有前途的改进方法涉及对样本进行丰富信息的获取。特别是，允许对强度进行数字化补充的定量相位成像（QPI）技术越来越受到关注。这种成像仅使用相位图像即可探索在强度图像中不可见的透明物体。另一个方向是使用染色图像来揭示强度图像中细胞的某些特征；在这种情况下，不利用相位信息。在本文中，我们质疑在对染色样本进行 QPI 采集时使用强度和相位的双模态信息的意义。我们考虑了使用深度神经网络（DNN）从染色血涂片检测疟原虫寄生的红细胞以诊断疟疾的问题。傅里叶相衬显微镜（FPM）被用作产生 QPI 图像的计算显微镜框架。我们表明，与仅使用强度图像相比，当 DNN 中的卷积通过基于复数的形式实现时，双模态信息将检测性能提高了 4%。这证明了 DNN 可以从双模态增强信息中受益。我们推测，这些结果应该扩展到通过 QPI 采集处理的其他应用。
