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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and

Fatma Krikid¹, Hugo Rositi², Antoine Vacavant¹

¹Institut Pascal, CNRS, Clermont Auvergne INP, Université Clermont Auvergne, F-63000 Clermont-Ferrand, France.

Journal of Imaging

|December 27, 2024

Summary

This summary is machine-generated.

Deep learning (DL) significantly improves microscopic image segmentation (MIS) for biological research. This review analyzes DL methods, datasets, and metrics, highlighting advancements in cell, nucleus, and tissue segmentation.

Keywords:

biology cell segmentation deep learning image segmentation microscopic image nucleus segmentation tissue analysis

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Medical Imaging
Biological Research
Computational Biology

Background:

Microscopic image segmentation (MIS) is crucial for analyzing cellular structures and tissues.
Traditional MIS methods face challenges like imaging variability, complex structures, and noise.
Deep learning (DL) has emerged as a powerful tool to overcome these limitations.

Purpose of the Study:

To provide a comprehensive systematic literature review (SLR) of state-of-the-art DL methods for MIS.
To analyze DL approaches for cell, nucleus, and tissue segmentation over the past six years.
To evaluate datasets and performance metrics used in DL-based MIS studies.

Main Methods:

Systematic literature review of DL methods for microscopic image segmentation.
Critical analysis of DL techniques addressing segmentation challenges.
Evaluation of datasets and performance metrics in selected studies.

Main Results:

DL methods have substantially advanced MIS, improving accuracy and efficiency.
Key DL contributions in cell, nucleus, and tissue segmentation were identified.
Commonly used datasets and performance metrics were analyzed.

Conclusions:

DL holds transformative potential for enhancing diagnostic accuracy and research efficiency in medical and biological applications.
The review identifies current advancements and gaps in DL-based MIS.
Future research directions are suggested to further improve methodologies and patient outcomes.