@article {69880, title = {DeepPD: Joint Phase and Object Estimation from Phase Diversity with Neural Calibration of a Deformable Mirror}, journal = {arXiv}, year = {2025}, month = {2025 Apr 19}, abstract = {

Sample-induced aberrations and optical imperfections limit the resolution of fluorescence microscopy. Phase diversity is a powerful technique that leverages complementary phase information in sequentially acquired images with deliberately introduced aberrations--the phase diversities--to enable phase and object reconstruction and restore diffraction-limited resolution. These phase diversities are typically introduced into the optical path via a deformable mirror. Existing phase-diversity-based methods are limited to Zernike modes, require large numbers of diversity images, or depend on accurate mirror calibration--which are all suboptimal. We present DeepPD, a deep learning-based framework that combines neural representations of the object and wavefront with a learned model of the deformable mirror to jointly estimate both object and phase from only five images. DeepPD improves robustness and reconstruction quality over previous approaches, even under severe aberrations. We demonstrate its performance on calibration targets and biological samples, including immunolabeled myosin in fixed PtK2 cells.

}, doi = {10.48550/arxiv.2504.14157}, url = {https://doi.org/10.48550/arxiv.2504.14157}, author = {Magdalena C. Schneider and Courtney Johnson and C{\'e}dric Allier and Larissa Heinrich and Diane Adjavon and Joren Husic and Patrick La Riviere and Stephan Saalfeld and Hari Shroff} } @article {69149, title = {Prediction of Cellular Identities from Trajectory and Cell Fate Information}, journal = {arXiv}, year = {2024}, month = {2024 Jan 11}, abstract = {

Determining cell identities in imaging sequences is an important yet challenging task. The conventional method for cell identification is via cell tracking, which is complex and can be time-consuming. In this study, we propose an innovative approach to cell identification during early C. elegans embryogenesis using machine learning. We employed random forest, MLP, and LSTM models, and tested cell classification accuracy on 3D time-lapse confocal datasets spanning the first 4 hours of embryogenesis. By leveraging a small number of spatial-temporal features of individual cells, including cell trajectory and cell fate information, our models achieve an accuracy of over 90\%, even with limited data. We also determine the most important feature contributions and can interpret these features in the context of biological knowledge. Our research demonstrates the success of predicting cell identities in 4D imaging sequences directly from simple spatio-temporal features.

}, doi = {10.48550/arXiv.2401.06182}, url = {https://doi.org/10.48550/arXiv.2401.06182}, author = {Baiyang Dai and Jiamin Yang and Hari Shroff and Patrick La Riviere} } @article {69282, title = {Three-dimensional spatio-angular fluorescence microscopy with a polarized dual-view inverted selective-plane illumination microscope (pol-diSPIM)}, journal = {bioRxiv}, year = {2024}, month = {2024 Mar 12}, abstract = {

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the three-dimensional orientations and diffraction-limited positions of ensembles of fluorescent dipoles that label biological structures, and we share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model our samples, their excitation, and their detection using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labelled giant unilamellar vesicles, fast-scarlet-labelled cellulose in xylem cells, and phalloidin-labelled actin in U2OS cells. Additionally, we observe phalloidin-labelled actin in mouse fibroblasts grown on grids of labelled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.Competing Interest StatementH.S., A.K., S.M., P.L.R., R.O., Y.W., and T.C. hold US Patent $\#$11428632.

}, doi = {10.1101/2024.03.09.584243}, url = {https://www.biorxiv.org/content/early/2024/03/12/2024.03.09.584243}, author = {Talon Chandler and Min Guo and Yijun Su and Jiji Chen and Yicong Wu and Junyu Liu and Atharva Agashe and Robert S. Fischer and Shalin B. Mehta and Abhishek Kumar and Tobias I. Baskin and Valentin Jamouille and Huafeng Liu and Vinay Swaminathan and Amrinder Nain and Rudolf Oldenbourg and Patrick La Riviere and Hari Shroff} }