对血液涂片图像中的红细胞进行精确分割是一项重要的技术，也是一个难题，主要是因为红细胞经常重叠，没有明显边界。针对此问题，本文提出一种基于U-Net++和神经常微分方程（Neural Ordinary Differential Equations，NODE）的深度学习网络NODE-UNet++用于红细胞的初步分割，再利用标记分水岭算法分割血液涂片图像中的粘连红细胞。首先对图像进行裁剪和标注，突出待分割区域；然后应用新的语义分割体系结构NODE-UNet++对预处理后的图像进行初始分割得到概率灰度图；最后采用标记分水岭算法将灰度图中的粘连红细胞分离，得到最终红细胞分割结果图。实验结果表明，Dice系数达到96.89%、平均像素准确率达到98.97%、平均交并比达到96.33%。通过对不同血液涂片图像的分割结果表明，该方法能高效精确地提取每个红细胞，满足后续红细胞图像处理的需求。

血红细胞的形貌特征是医学领域对多种疾病进行预防和诊断的一项重要指标，提出一种同步移相显微干涉法实现对血红细胞形貌的动态测量。搭建了透射式显微干涉成像系统，测量了100 μm内径模拟微血管内、名义直径为7 μm～8 μm、高度最大值为2 μm的新西兰兔血红细胞，针对血红细胞所处的微血管环境提出了基于微血管相位相减的血红细胞形貌提取方法和成像放大率校正方法，实验得到模拟微血管内的血红细胞平均直径7.757 μm和平均最高高度2.022 μm，验证了本方法具有在体定量测量血红细胞形貌的潜力。

We accurately reconstruct three-dimensional (3-D) refractive index (RI) distributions from highly ill-posed two-dimensional (2-D) measurements using a deep neural network (DNN). Strong distortions are introduced on reconstructions obtained by the Wolf transform inversion method due to the ill-posed measurements acquired from the limited numerical apertures (NAs) of the optical system. Despite the recent success of DNNs in solving ill-posed inverse problems, the application to 3-D optical imaging is particularly challenging due to the lack of the ground truth. We overcome this limitation by generating digital phantoms that serve as samples for the discrete dipole approximation (DDA) to generate multiple 2-D projection maps for a limited range of illumination angles. The presented samples are red blood cells (RBCs), which are highly affected by the ill-posed problems due to their morphology. The trained network using synthetic measurements from the digital phantoms successfully eliminates the introduced distortions. Most importantly, we obtain high fidelity reconstructions from experimentally recorded projections of real RBC sample using the network that was trained on digitally generated RBC phantoms. Finally, we confirm the reconstruction accuracy using the DDA to calculate the 2-D projections of the 3-D reconstructions and compare them to the experimentally recorded projections.