Tímea Tóth

Microenvironmental information significantly improves the recognition of cell types in human lung cancer patients

This study improves cell type classification in lung cancer tissue sections by incorporating microenvironment information through fisheye transformations, achieving human-level accuracy.

HCS-3DX, a next-generation AI-driven automated 3D-oid high-content screening system

HCS-3DX is a new AI-driven system for 3D-oid high-content screening that improves resolution and single-cell analysis in 3D tumor models.

When the pen is mightier than the sword: semi-automatic 2 and 3D image labelling

The paper introduces a semi-automatic annotation tool for 2D and 3D image labelling to reduce expert effort in creating object-based labels.

Show me your neighbour and I tell what you are: fisheye transformation for deep learning-based single-cell phenotyping

The paper introduces a fisheye transformation for single-cell phenotyping, improving accuracy by weighting nearby cells more heavily than distant ones, and validates it on microscopy and wildlife datasets.

A deep learning framework for nucleus segmentation using image style transfer

The paper introduces a deep learning framework for nucleus segmentation that uses image style transfer to adapt to new data without expert annotations, outperforming existing methods on diverse microscopy images.

3D-Cell-Annotator: an open-source active surface tool for single cell segmentation in 3D microscopy images

3D-Cell-Annotator provides a tool for automated 3D cell segmentation in microscopy images using active surfaces and shape descriptors, achieving expert-level precision.

Show me your neighbours, and I’ll tell you what you are – cellular microenvironment matters

The paper shows that incorporating microenvironmental features improves machine learning-based single-cell phenotyping in imaging data.