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Updated: Dec 26, 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

Sparse Data-Driven Learning for Effective and Efficient Biomedical Image Segmentation.

John A Onofrey^1,2, Lawrence H Staib^1,3, Xiaojie Huang^1,4

¹Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut 06520, USA;

Annual Review of Biomedical Engineering

|March 15, 2020

Summary

This summary is machine-generated.

Sparsity techniques improve high-dimensional machine learning efficiency. These methods are well-suited for medical image segmentation and quantification tasks.

Keywords:

dictionary learning image representation image segmentation machine learning medical image analysis sparsity

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Area of Science:

Machine Learning
Medical Imaging
Computer Vision

Background:

High-dimensional data presents challenges in machine learning.
Sparsity offers a powerful framework for enhancing efficiency.
Medical image segmentation requires robust and efficient methods.

Purpose of the Study:

To explore sparsity-based techniques for medical image segmentation.
To demonstrate the utility of sparsity in improving computational and representational efficiency.
To present methods applicable to medical image segmentation and quantification.

Main Methods:

Dictionary learning strategies for sparsity.
Deep learning approaches incorporating sparsity.
Application of sparsity to medical image segmentation.

Main Results:

Sparsity enhances representational efficiency in high-dimensional data.
Sparsity-based methods are effective for medical image segmentation.
Techniques facilitate accurate medical image quantification.

Conclusions:

Sparsity is a key concept for efficient machine learning in high dimensions.
Sparsity-based methods offer significant advantages for medical image analysis.
The presented techniques are valuable for segmentation and quantification tasks.