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Updated: Jun 18, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Genome analysis through image processing with deep learning models.

Yao-Zhong Zhang¹, Seiya Imoto²

¹Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. yaozhong@hgc.jp.

Journal of Human Genetics

|July 31, 2024

Summary

This summary is machine-generated.

Genomic sequence analysis is enhanced by converting DNA data into images. This review explores image-based deep learning methods for genome analysis, highlighting their advantages.

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Area of Science:

Genomics
Bioinformatics
Computer Vision

Background:

Genomic sequences are traditionally represented as strings (A, C, G, T).
Image representations like Chaos Game Representation (CGR) and read pileups offer alternative data visualization.
Deep learning (DL) methods are increasingly applied to complex biological data.

Purpose of the Study:

To review image processing techniques combined with deep learning for genomic sequence analysis.
To summarize key applications and benefits of using image-based DL in genomics.
To provide insights into the evolving landscape of genomic data interpretation.

Main Methods:

Review of existing literature on image-based deep learning for genomics.
Analysis of three typical applications utilizing image processing and DL models.
Examination of data encoding strategies and spatial information integration.

Main Results:

Image-based deep learning methods offer novel ways to analyze genomic data.
Applications demonstrate the potential of visual representations in uncovering sequence-related information.
These approaches leverage advancements in computer vision and natural language processing.

Conclusions:

Image-based deep learning presents a promising avenue for advanced genome analysis.
The integration of visual data processing enhances the interpretation of complex genomic information.
Further exploration of these methods can lead to significant discoveries in genomic research.