Atlas-based neuroinformatics via MRI: Harnessing information from past clinical cases and quantitative image analysis for patient care

Research output: Contribution to journalArticle

Abstract

With the ever-increasing amount of anatomical ormation radiologists have to evaluate for routine diagnoses, computational port that facilitates more efficient education and clinical decision making is highly desired. Despite the rapid progress of image analysis technologies for magnetic resonance imaging of the human brain, these methods have not been widely adopted for clinical diagnoses. To bring computational port into the clinical arena, we need to understand the decision-making process employed by well-trained clinicians and develop tools to simulate that process. In this review, we discuss the potential of atlas-based clinical neuroormatics, which consists of annotated databases of anatomical measurements grouped according to their morphometric phenotypes and coupled with the clinical ormatics upon which their diagnostic groupings are based. As these are indexed via parametric representations, we can use image retrieval tools to search for phenotypes along with their clinical metadata. The review covers the current technology, preliminary data, and future directions of this field.

Original languageEnglish (US)
Pages (from-to)71-92
Number of pages22
JournalAnnual Review of Biomedical Engineering
Volume15
DOIs
StatePublished - Jul 2013

Fingerprint

Atlases
Magnetic resonance imaging
Image analysis
Patient Care
Decision making
Technology
Phenotype
Image retrieval
Magnetic resonance
Metadata
Brain
Decision Making
Education
Magnetic Resonance Imaging
Databases
Imaging techniques
Radiologists
Direction compound
Clinical Decision-Making

Keywords

  • diagnosis
  • morphometry
  • neuroanatomy
  • neurology

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biomedical Engineering

Cite this

@article{761338bb353043ee8cd1a47e2057e6cd,
title = "Atlas-based neuroinformatics via MRI: Harnessing information from past clinical cases and quantitative image analysis for patient care",
abstract = "With the ever-increasing amount of anatomical ormation radiologists have to evaluate for routine diagnoses, computational port that facilitates more efficient education and clinical decision making is highly desired. Despite the rapid progress of image analysis technologies for magnetic resonance imaging of the human brain, these methods have not been widely adopted for clinical diagnoses. To bring computational port into the clinical arena, we need to understand the decision-making process employed by well-trained clinicians and develop tools to simulate that process. In this review, we discuss the potential of atlas-based clinical neuroormatics, which consists of annotated databases of anatomical measurements grouped according to their morphometric phenotypes and coupled with the clinical ormatics upon which their diagnostic groupings are based. As these are indexed via parametric representations, we can use image retrieval tools to search for phenotypes along with their clinical metadata. The review covers the current technology, preliminary data, and future directions of this field.",
keywords = "diagnosis, morphometry, neuroanatomy, neurology",
author = "Susumu Mori and Kenichi Oishi and Faria, {Andreia Vasconcellos} and Miller, {Michael I.}",
year = "2013",
month = "7",
doi = "10.1146/annurev-bioeng-071812-152335",
language = "English (US)",
volume = "15",
pages = "71--92",
journal = "Annual Review of Biomedical Engineering",
issn = "1523-9829",
publisher = "Annual Reviews Inc.",

}

TY - JOUR

T1 - Atlas-based neuroinformatics via MRI

T2 - Harnessing information from past clinical cases and quantitative image analysis for patient care

AU - Mori, Susumu

AU - Oishi, Kenichi

AU - Faria, Andreia Vasconcellos

AU - Miller, Michael I.

PY - 2013/7

Y1 - 2013/7

N2 - With the ever-increasing amount of anatomical ormation radiologists have to evaluate for routine diagnoses, computational port that facilitates more efficient education and clinical decision making is highly desired. Despite the rapid progress of image analysis technologies for magnetic resonance imaging of the human brain, these methods have not been widely adopted for clinical diagnoses. To bring computational port into the clinical arena, we need to understand the decision-making process employed by well-trained clinicians and develop tools to simulate that process. In this review, we discuss the potential of atlas-based clinical neuroormatics, which consists of annotated databases of anatomical measurements grouped according to their morphometric phenotypes and coupled with the clinical ormatics upon which their diagnostic groupings are based. As these are indexed via parametric representations, we can use image retrieval tools to search for phenotypes along with their clinical metadata. The review covers the current technology, preliminary data, and future directions of this field.

AB - With the ever-increasing amount of anatomical ormation radiologists have to evaluate for routine diagnoses, computational port that facilitates more efficient education and clinical decision making is highly desired. Despite the rapid progress of image analysis technologies for magnetic resonance imaging of the human brain, these methods have not been widely adopted for clinical diagnoses. To bring computational port into the clinical arena, we need to understand the decision-making process employed by well-trained clinicians and develop tools to simulate that process. In this review, we discuss the potential of atlas-based clinical neuroormatics, which consists of annotated databases of anatomical measurements grouped according to their morphometric phenotypes and coupled with the clinical ormatics upon which their diagnostic groupings are based. As these are indexed via parametric representations, we can use image retrieval tools to search for phenotypes along with their clinical metadata. The review covers the current technology, preliminary data, and future directions of this field.

KW - diagnosis

KW - morphometry

KW - neuroanatomy

KW - neurology

UR - http://www.scopus.com/inward/record.url?scp=84880340041&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880340041&partnerID=8YFLogxK

U2 - 10.1146/annurev-bioeng-071812-152335

DO - 10.1146/annurev-bioeng-071812-152335

M3 - Article

C2 - 23642246

AN - SCOPUS:84880340041

VL - 15

SP - 71

EP - 92

JO - Annual Review of Biomedical Engineering

JF - Annual Review of Biomedical Engineering

SN - 1523-9829

ER -