Biology subjects

Korovin, S.

Publications and source records attributed to Korovin, S..

4 recordsLinked to original sources

bioRxiv2026

Quantifying the spatio-temporal image degradation under motion blur in fluorescence microscopy

The spatial resolution of optical imaging systems is fundamentally restricted by the diffraction limit. However, in widefield live-cell microscopy, the achievable resolution is further constrained by the specimen motion, which indicates the existence of a fundamental spatio-temporal resolution trade-off between signal accumulation during the full frame integration and the resulting motion blur. To improve the fidelity with which moving objects can be imaged, a quantitative understanding of this spatio-temporal trade-off is necessary. Here, we present a systematic analysis of motion-induced resolution dynamics measured with spectral signal-to-noise ratio (SSNR). We developed a simulation framework which models the image formation of objects undergoing arbitrary motion, to evaluate the degradation of the spatial resolution under translational and rotational dynamics. Our results demonstrate that for translating objects, the spatial resolution is anisotropically reduced as a function of the orientation of the object relative to the motion vector, leading to the spectral signal-to-noise ratio degrading by up to 50% and the resolution by up to 40% for a 90{degrees} change in the motion direction. Furthermore, we show that for rotational motion, conventional radially averaged metrics such as the Fourier Ring Correlation are not able to quantify the effects of angular blur. On the other hand, the SSNR is able to accurately quantify this degradation. These findings underscore the necessity of an object-oriented imaging approach, in which acquisition parameters such as exposure time are tuned to specific biological spatio-temporal characteristics to optimize the trade-off between motion blur and spatial fidelity.

biophysics↗

bioRxiv2025

Reducing Manual Labour in Forensic Microtrace Recognition with Deep Learning

Forensic microtrace investigation relies on time- and labor-intensive microscopic analyses. To aid forensic experts in their investigations, an image recognition model for microtrace localisation and classification is needed. In this work, we use deep learning to automate trace recognition in images captured with automated microscopy. We localise and classify fibres, hairs, skin, glass and sand in microscopy scans through pixel-wise classification of tape-lift samples. As deep learning requires extensive amounts of annotated training data, we additionally investigate various pretraining strategies to minimise the required annotation workload. We compare ImageNet pretraining, pretraining with self-supervised learning and a sequential application of these approaches. We find that pretrained models are able to reduce the required annotated data twofold compared to models trained from scratch while retaining the prediction accuracy. While our ImageNet-pretrained models outperform our self-supervised-pretrained models, we achieve the highest accuracy by combining the two approaches, resulting in a factor of 4 reduction of manual annotated microtraces or a 65 % improvement in recognition and localisation accuracy (mean intersection over union increases from 0.34 to 0.56 due to pretraining) when training on only 2.2 dm2 of annotated tape lift scans. Our model is, therefore, the method of choice for the automatic analysis of large forensic microtrace scans.

bioinformatics↗

bioRxiv2024

CLEAR-IT: Contrastive Learning Enabled Accurate Registration of Immune and Tumor cells from multiplexed images with limited labels in a platform-independent manner

Accurate phenotyping of cells in the tumor microenvironment is crucial for understanding cancer biology and developing effective therapies. However, current methods require precise cell segmentations and struggle to generalize across different imaging modalities, limiting their utility in digital pathology. Here, we show that Contrastive Learning Enabled Accurate Registration of Immune and Tumor Cells (CLEAR-IT) overcomes these limitations, providing a robust and versatile tool for cell phenotyping. CLEAR-IT accurately phenotypes cells comparable to state-of-the-art methods, generalizes across multiplex imaging modalities, maintains high performance even with limited number of labels, and enables the extraction of prognostic markers. Additionally, CLEAR-IT can be combined with existing methods to boost their performance, whereas its lack of need for precise cell segmentations significantly reduces training efforts. This method enhances the robustness and efficiency of digital pathology workflows, making it a valuable tool for cancer research and diagnostics.

bioinformatics↗

bioRxiv2024

Using splines for point spread function calibration atnon-uniform depths in localization microscopy

Single-molecule localization microscopy methods extensively leverage the microscope point spread function (PSF) for fitting the molecules. Calibrating an accurate PSF model is especially difficult in the presence of depth-dependent aberrations which alter the PSF shape depending on the imaging depth. The aberrations at depths of a few micrometers become substantial enough to considerably impoverish the conventional calibration methods performance. In our work, we propose a novel spline model which enables the depth-dependent PSF model calibration by interpolating between the beads at arbitrary depths. We show that diffspline reduces the PSF intensity overestimation by 67.8 percentage points and underestimation by 21.8 percentage points. Moreover, it eliminates the depth-dependent bias and improves the localization precision two-fold compared to previous approaches.

biophysics↗