Capillaroscopy is a simple microscopy technique able to measure important clinical biomarkers non-invasively. For example, optical absorption gaps between red blood cells in capillary vessels of the nailfold have been shown to correlate with severity of neutropenia. The direct visualization of individual white blood cells with capillaroscopic techniques is elusive because it is challenging to generate epiillumination phase contrast in thick turbid media. Here, we evaluate white blood cell visibility with graded-field capillaroscopy in a flow phantom. We fabricate capillary phantoms with soft photolithography using PDMS doped with TiO2 and India ink to emulate skin optical properties. These glass-free phantoms feature channels embedded in scattering media at controlled depths (70-470 μm), as narrow as 15 x 15 μm, and permit blood flow up to 6 mm/s. We optimize the contrast of the graded-field capillaroscope in these tissue-realistic phantoms and demonstrate high speed imaging (200 Hz) of blood cells flowing through scattering media.
Automated segmentation of tissue and cellular structure in H&E images is an important first step towards automated histopathology slide analysis. For example, nuclei segmentation can aid with detecting pleomorphism and epithelium segmentation can aid in identification of tumor infiltrating lymphocytes etc. Existing deep learning-based approaches are often trained organ-wise and lack diversity of training data for multi-organ segmentation networks. In this work, we propose to augment existing nuclei segmentation datasets using cycleGANs. We learn an unpaired mapping from perturbed randomized polygon masks to pseudo-H&E images. We generate over synthetic H&E patches from several different organs for nuclei segmentation. We then use an adversarial U-Net with spectral normalization for increased training stability for segmentation. This paired image-to-image translation style network not only learns the mapping form H&E patches to segmentation masks but also learns an optimal loss function. Such an approach eliminates the need for a hand-crafted loss which has been explored significantly for nuclei segmentation. We demonstrate that the average accuracy for multi-organ nuclei segmentation increases to 94.43% using the proposed synthetic data generation and adversarial U-Net-based segmentation pipeline as compared to 79.81% when no synthetic data and adversarial loss was used.
Febrile neutropenia (FN) is a common cause of hospitalization for cancer patients undergoing chemotherapy treatment. To screen for FN, patients require invasive blood draws and complete blood cell counts, which increases risk of nosocomial infection while in an immunocompromised state. There is a pressing clinical need for non-invasive, point-of-care technology to frequently screen for FN, which, if detected early, can be prophylactically managed. A promising approach to address this need is capillaroscopy, through which blood cells are imaged in capillaries non-invasively. Visualization of shadows caused by absorption of individual red blood cells is currently achievable, and correlation between the absence of optical absorption gaps and severe neutropenia has been observed. However, a completely accurate identification of the physical origin of these optical absorption gaps for conclusive neutropenia diagnosis remains an elusive task. Here we present scattering oblique plane microscopy as a means of imaging moving scattering particles within a turbid medium with the goal of eventually imaging and characterizing blood cells in vivo flowing in superficial capillaries. Our imaging system illuminates an oblique light sheet through a capillary bed and collects back-scatter using a single objective at frame rates of >200 Hz. To validate this system, we develop phantoms mimicking capillaries with 200 μm diameter lumens embedded deep in silicone doped with TiO2 and India ink. Single 3 μm diameter polystyrene beads flowing through the capillaries are resolved with a signal to noise ratio of approximately 5:1 at a depth of 1 mean free path.
Wavefront sensing is typically accomplished with a Shack-Hartmann wavefront sensor (SHWS), where a CCD or CMOS is placed at the focal plane of a periodic, microfabricated lenslet array. Tracking the displacement of the resulting spots in the presence of an aberrated wavefront yields measurement of the relative wavefront introduced. A SHWS has a fundamental tradeoff between sensitivity and range, determined by the pitch and focal length of its lenslet array, such that the number of resolvable tilts is a constant. Recently, diffuser wavefront sensing (DWS) has been demonstrated by measuring the lateral shift of a coherent speckle pattern using the concept of the diffuser memory effect. Here we demonstrate that tracking distortions of the non-periodic caustic pattern produced by a holographic diffuser allows accurate autorefraction of a model eye with a number of resolvable tilts that extends beyond the fundamental limit of a SHWS. Using a multi-level Demon’s image registration algorithm, we are able to demonstrate that a DWS demonstrates a 2.5x increase in number of resolvable prescriptions as compared to a conventional SHWS while maintaining acceptable accuracy and repeatability for eyeglass prescriptions. We evaluate the performance of a DWS and SHWS in parallel with a coherent laser diode without (LD) and with a laser speckle reducer (LD+LSR), and an incoherent light-emitting diode (LED), demonstrating caustic-tracking is compatible with coherent and incoherent sources. Additionally, the DWS diffuser costs 40x less than a SHWS lenslet array, enabling affordable large-dynamic range autorefraction without moving parts.