用于高效生物医学图像分割的稀疏数据驱动学习。

IF 12.8 1区工程技术 Q1 ENGINEERING, BIOMEDICAL

Annual Review of Biomedical Engineering Pub Date : 2020-06-04 Epub Date: 2020-03-13 DOI:10.1146/annurev-bioeng-060418-052147

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

{"title":"用于高效生物医学图像分割的稀疏数据驱动学习。","authors":"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","DOIUrl":null,"url":null,"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.","PeriodicalId":50757,"journal":{"name":"Annual Review of Biomedical Engineering","volume":"22 ","pages":"127-153"},"PeriodicalIF":12.8000,"publicationDate":"2020-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1146/annurev-bioeng-060418-052147","citationCount":"4","resultStr":"{\"title\":\"Sparse Data-Driven Learning for Effective and Efficient Biomedical Image Segmentation.\",\"authors\":\"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\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":50757,\"journal\":{\"name\":\"Annual Review of Biomedical Engineering\",\"volume\":\"22 \",\"pages\":\"127-153\"},\"PeriodicalIF\":12.8000,\"publicationDate\":\"2020-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1146/annurev-bioeng-060418-052147\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual Review of Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1146/annurev-bioeng-060418-052147\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2020/3/13 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1146/annurev-bioeng-060418-052147","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/3/13 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}

引用次数: 4

摘要

稀疏性是一个强大的概念，可用于高维机器学习以及相关的表征和计算效率。稀疏性非常适合医学图像分割。我们介绍了一系列结合稀疏性的技术，包括基于字典学习和深度学习的策略，这些技术主要用于医学图像分割和相关量化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Annual Review of Biomedical Engineering 工程技术-工程：生物医学

CiteScore

18.80

自引率

0.00%

发文量

期刊介绍： Since 1999, the Annual Review of Biomedical Engineering has been capturing major advancements in the expansive realm of biomedical engineering. Encompassing biomechanics, biomaterials, computational genomics and proteomics, tissue engineering, biomonitoring, healthcare engineering, drug delivery, bioelectrical engineering, biochemical engineering, and biomedical imaging, the journal remains a vital resource. The current volume has transitioned from gated to open access through Annual Reviews' Subscribe to Open program, with all articles published under a CC BY license.