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

John A. Onofrey; Lawrence H. Staib; Xiaojie Huang; Fan Zhang; Xenophon Papademetris; Dimitris Metaxas; Daniel Rueckert; James S. Duncan

doi:10.1146/annurev-bioeng-060418-052147

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

John A. Onofrey, Lawrence H. Staib, Xiaojie Huang, Fan Zhang, Xenophon Papademetris, Dimitris Metaxas, Daniel Rueckert, James S. Duncan

Research output: Contribution to journal › Review article › peer-review

7 Scopus citations

Abstract

Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.

Original language	English
Pages (from-to)	127-153
Number of pages	27
Journal	Annual Review of Biomedical Engineering
Volume	22
DOIs	https://doi.org/10.1146/annurev-bioeng-060418-052147
State	Published - 4 Jun 2020
Externally published	Yes

Keywords

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

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10.1146/annurev-bioeng-060418-052147

Cite this

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abstract = "Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.",

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AU - Onofrey, John A.

AU - Staib, Lawrence H.

AU - Huang, Xiaojie

AU - Zhang, Fan

AU - Papademetris, Xenophon

AU - Metaxas, Dimitris

AU - Rueckert, Daniel

AU - Duncan, James S.

PY - 2020/6/4

Y1 - 2020/6/4

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AB - Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.

KW - Sparsity

KW - dictionary learning

KW - image representation

KW - image segmentation

KW - machine learning

KW - medical image analysis

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DO - 10.1146/annurev-bioeng-060418-052147

M3 - Review article

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JF - Annual Review of Biomedical Engineering

ER -

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

Abstract

Keywords

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