Charles Kervrann A

This paper introduces Nagini3D, a 3D image segmentation method combining deep learning and active surfaces for improved shape analysis and consistency across 3D volumes.

This paper introduces ExoDeepFinder, a deep learning method for detecting dynamic exocytosis events in TIRFM images, showing better performance and adaptability than conventional methods.

This paper introduces Template Learning, a deep learning method for particle picking in cryo-ET that reduces reliance on annotated datasets by simulating training data.

DeepCristae restores mitochondria cristae in low-resolution microscopy images using a CNN trained on STED data and specialized loss functions.

The paper develops an automated system for detecting rare chromosomal aberrations in microscopy images using deep learning, sparse representation, and dimensionality reduction, achieving improved performance over existing methods.

BioImageIT integrates FAIR data management with analysis to address challenges in bioimaging data sharing and annotation.

This paper explores unsupervised classification of macromolecular complexes in cryo-ET data using transfer learning and deep learning, showing promising results on real data without manual annotation.

STracking is a Python library that streamlines particle tracking by combining algorithms into standardized pipelines, offering a user-friendly interface for analysis.

The paper introduces SPITFIR(e), an algorithm for restoring 2D-3D fluorescence images/videos and subtracting background, demonstrating its effectiveness across multiple microscopy modalities.

This paper introduces DeepFinder, a deep learning method that improves macromolecule identification in 3D cellular cryo-ET data by localizing multiple species with high accuracy.

The paper compares high-resolution image reconstruction methods for confocal microscopy, introducing a new deconvolution algorithm that improves resolution, contrast, and spatial intensity homogeneity.