Two-dimensional observation of biological samples at hundreds of nanometers resolution or even below is of high interest for many sensitive medical applications. Recent advances have been obtained over the last ten years with computational imaging. Among them, Fourier Ptychographic Microscopy is of particular interest because of its important super-resolution factor. In complement to traditional intensity images, phase images are also produced. A large set of $\mathcal{N}$ raw images (with typically $\mathcal{N}$ = 225) is, however, required because of the reconstruction process that is involved. In this paper, we address the problem of FPM image reconstruction using a few raw images only (here, $\mathcal{N}$ = 37) as is highly desirable to increase microscope throughput. In contrast to previous approaches, we develop an algorithmic approach based on a physics-informed optimization deep neural network and statistical reconstruction learning. We demonstrate its efficiency with the help of simulations. The forward microscope image formation model is explicitly introduced in the deep neural network model to optimize its weights starting from an initialization that is based on statistical learning. The simulation results that are presented demonstrate the conceptual benefits of the approach. We show that high-quality images are effectively reconstructed without any appreciable resolution degradation. The learning step is also shown to be mandatory.

Digital pathology based on a whole slide imaging system is about to permit a major breakthrough in automated diagnosis for rapid and highly sensitive disease detection. High-resolution FPM (Fourier ptychographic microscopy) slide scanners delivering rich information on biological samples are becoming available. They allow new effective data exploitation for efficient automated diagnosis. However, when the sample thickness becomes comparable to or greater than the microscope depth of field, we report an observation of undesirable contrast change of sub-cellular compartments in phase images around the optimal focal plane, reducing their usability. In this article, a bi-modal U-Net artificial neural network (i.e., a two channels U-Net fed with intensity and phase images) is trained to reinforce specifically targeted sub-cellular compartments contrast for both intensity and phase images. The procedure used to construct a reference database is detailed. It is obtained by exploiting the FPM reconstruction algorithm to explore images around the optimal focal plane with virtual Z-stacking calculations and selecting those with adequate contrast and focus. By construction and once trained, the U-Net is able to simultaneously reinforce targeted cell compartment visibility and compensate for any focus imprecision. It is efficient over a large field of view at high resolution. The interest of the approach is illustrated considering the use-case of Plasmodium falciparum detection in blood smear where improvement in the detection sensitivity is demonstrated without degradation of the specificity. Post-reconstruction FPM image processing with such U-Net and its training procedure is general and applicable to demanding biological screening applications.

2D observation of biological samples at hundreds of nanometers resolution or even below is of high-interest for many sensitive medical applications. Recent advances have been obtained with computational imaging and we consider here Fourier Ptychographic Microscopy (FPM). Although this technique has permitted to attain an impressive super-resolution factor up to 6 and to assess at the same time local optical thickness of samples, it suffers from low measurement cadence. This limits its applicability as a routine diagnostic tool. In this paper, we address the specific problem of FPM reconstruction when only few low resolution images are acquired. To this end, we introduce a physics informed optimization Deep Neural Network that is combined to statistical reconstruction learning. More precisely, the forward image microscope formation model is explicitly introduced in the model to optimize its weights starting from an initialization that is based on a statistical learning. This combination brings some robustness to the reconstruction process. The presented simulation results for an FPM configuration with very low overlap factor (Γ ∼ 10%) demonstrate the conceptual benefits of the approach in terms of image quality and resolution. The learning step is also shown to be mandatory.

Digital pathology based on whole slide imaging system is about to permit major breakthrough in automated diagnosis for a rapid and highly sensitive disease detection. High-resolution FPM (Fourier Ptychographic Microscopy) slide scanners delivering rich information on the sample such as intensity and its complementary phase image are becoming available. They allow new effective bi-modal data exploitation strategies that can be useful for more efficient automated diagnosis. However, when the thickness of the sample becomes comparable or greater than the microscope depth of field, we report observation of undesirable contrast change of sub-cellular compartments in phase images around optimal focus plan, reducing their usability. In this article, an U-Net artificial neural network is trained to reinforce specifically targeted sub-cellular compartments contrast (for both intensity and phase images). The procedure used to construct a reference database with virtual z-stacking calculations is detailed. It is obtained exploiting FPM reconstruction algorithm to explore images around optimal focus plane (virtual Z-stacking) and select those with adequate contrast and focus. Furthermore intensity and phase images are advantageously exploited in a bi-modal deep-neural network architecture. By construction and once trained, the U-Net is able to simultaneously reinforce targeted cell compartments visibility and to compensate any focus imprecision. It is efficient over a large field of view at high resolution. The interest of the approach is illustrated considering the use-case of Plasmodium falciparum detection in blood smear where improvement in the detection sensitivity is demonstrated without degradation of the specificity. Post-reconstruction FPM image processing with such U-Net and its training procedure is general and applicable to demanding biological screening applications.

Digital pathology based on whole slide imaging system is about to permit major breakthrough in automated diagnosis for a rapid and highly sensitive disease detection. High-resolution FPM (Fourier Ptychographic Microscopy) slide scanners delivering rich information on biological samples are becoming available. They allow new effective data exploitation for efficient automated diagnosis. However, when the sample thickness becomes comparable or greater than the microscope depth of field, we report observation of undesirable contrast change of sub-cellular compartments in phase images around optimal focal plane, reducing their usability. In this article, a bi-modal U-Net artificial neural network (i.e. a two channels U-Net fed with intensity and phase images) is trained to reinforce specifically targeted sub-cellular compartments contrast for both intensity and phase images. The procedure used to construct a reference database is detailed. It is obtained exploiting FPM reconstruction algorithm to explore images around optimal focal plane with virtual Z-stacking calculations and selecting those with adequate contrast and focus. By construction and once trained, the U-Net is able to simultaneously reinforce targeted cell compartments visibility and to compensate any focus imprecision. It is efficient over a large field of view at high resolution. The interest of the approach is illustrated considering the use-case of Plasmodium falciparum detection in blood smear where improvement in the detection sensitivity is demonstrated without degradation of the specificity. Post-reconstruction FPM image processing with such U-Net and its training procedure is general and applicable to demanding biological screening applications.

Digital pathology based on whole slide imaging system is about to permit major breakthrough in automated diagnosis for a rapid and highly sensitive disease detection. High-resolution FPM (Fourier Ptychographic Microscopy) slide scanners delivering rich information on biological samples are becoming available. They allow new effective data exploitation for efficient automated diagnosis. However, when the sample thickness becomes comparable or greater than the microscope depth of field, we report observation of undesirable contrast change of sub-cellular compartments in phase images around optimal focal plane, reducing their usability. In this article, a bi-modal U-Net artificial neural network (i.e. a two channels U-Net fed with intensity and phase images) is trained to reinforce specifically targeted sub-cellular compartments contrast for both intensity and phase images. The procedure used to construct a reference database is detailed. It is obtained exploiting FPM reconstruction algorithm to explore images around optimal focal plane with virtual Z-stacking calculations and selecting those with adequate contrast and focus. By construction and once trained, the U-Net is able to simultaneously reinforce targeted cell compartments visibility and to compensate any focus imprecision. It is efficient over a large field of view at high resolution. The interest of the approach is illustrated considering the use-case of Plasmodium falciparum detection in blood smear where improvement in the detection sensitivity is demonstrated without degradation of the specificity. Post-reconstruction FPM image processing with such U-Net and its training procedure is general and applicable to demanding biological screening applications.