Work

Synthetic data generation and Mask-RCNN for Transmission Electron Microscope Image Segmentation

Year: 2024

Type: conference-paper

Abstract: user-friendly interface for the generation of synthetic data adaptable to a wide variety of transmission electron microscopy (TEM) data.This interface will include a built-in trainable model which can be tuned and refined with user generated synthetic data, facilitating broader accessibility to automated image segmentation for researchers with electron microscope data.

Source: BIO Web of Conferences

DOI: 10.1051/bioconf/202412910011

Authors: Natalia Da Silva De Sa, Andrew Stewart

Institutions: University of Limerick, University College London

Language: English

Topic: Advanced Electron Microscopy Techniques and Applications

Subfield: Structural Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: diamond

OpenAlex ID: W4403496080

FWCI:

Cites: 2

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Related to: 10