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Microscopy

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Sparse phase retrieval using a physics-informed neural network for Fourier ptychographic microscopy.

Optics letters
In this paper, we report a sparse phase retrieval framework for Fourier ptychographic microscopy using the recently proposed principle of physics-informed neural networks. The phase retrieval problem is cast as training bidirectional mappings from th...

Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution.

Optics express
Dipole-spread function (DSF) engineering reshapes the images of a microscope to maximize the sensitivity of measuring the 3D orientations of dipole-like emitters. However, severe Poisson shot noise, overlapping images, and simultaneously fitting high...

Lensless computational imaging with a hybrid framework of holographic propagation and deep learning.

Optics letters
Lensless imaging has attracted attention as it avoids the bulky optical lens. Lensless holographic imaging is a type of a lensless imaging technique. Recently, deep learning has also shown tremendous potential in lensless holographic imaging. A label...

Single-frame 3D lensless microscopic imaging via deep learning.

Optics express
Since the pollen of different species varies in shape and size, visualizing the 3-dimensional structure of a pollen grain can aid in its characterization. Lensless sensing is useful for reducing both optics footprint and cost, while the capability to...

Single-exposure height-recovery structured illumination microscopy based on deep learning.

Optics letters
Modulation-based structured illumination microscopy (SIM) is performed to reconstruct three-dimensional (3D) surface topography. Generally speaking, modulation decoding algorithms mainly include a phase-shift (PS) method and frequency analysis techni...

A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Accurate segmentation of nuclei is an essential step in analysis of digital histology images for diagnostic and prognostic applications. Despite recent advances in automated frameworks for nuclei segmentation, this task is still challenging. Specific...

Prediction of Human Induced Pluripotent Stem Cell Formation Based on Deep Learning Analyses Using Time-lapse Brightfield Microscopy Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We use deep learning methods to predict human induced pluripotent stem cell (hiPSC) formation using time-lapse brightfield microscopy images taken from a cell identified as the beginning of entered into the reprogramming process. A U-net is used to s...

Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning.

Journal of biomedical optics
SIGNIFICANCE: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug tes...

[Digital pathology].

Ugeskrift for laeger
Digitalisation of pathology slides allows pathologists to make diagnoses using a high-resolution computer screen instead of a conventional microscope. In 2020/21, the four pathology departments in the Region of Southern Denmark implemented digital pa...