MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
Artikel i vetenskaplig tidskrift, 2015

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

Författare

A. M. Mendrik

Image Sciences Institute

K. L. Vincken

Image Sciences Institute

H. J. Kuijf

Image Sciences Institute

M. Breeuwer

Technische Universiteit Eindhoven

Philips Healthcare Nederland

W. H. Bouvy

University Medical Center Utrecht

J. de Bresser

University Medical Center Utrecht

A. Alansary

University of Louisville

M. de Bruijne

Erasmus University Medical Center

Köbenhavns Universitet

A. Carass

Johns Hopkins University

A. El-Baz

University of Louisville

A. Jog

Johns Hopkins University

R. Katyal

LNM Institute of Information Technology (Deemed University), Jaipur

A. R. Khan

Western University

Robarts Research Institute

F. van der Lijn

Erasmus University Medical Center

Mahmood Qaiser

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Biomedicinsk elektromagnetik

R. Mukherjee

The Johns Hopkins University Applied Physics Laboratory

A. van Opbroek

Erasmus University Medical Center

S. Paneri

LNM Institute of Information Technology (Deemed University), Jaipur

S. Pereira

Universidade do Minho

Mikael Persson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

M. Rajchl

Robarts Research Institute

Imperial College London

D. Sarikaya

University at Buffalo, State University of New York

O. Smedby

Linköpings universitet

C. A. Silva

Universidade do Minho

H. A. Vrooman

Erasmus University Medical Center

S. Vyas

The Johns Hopkins University Applied Physics Laboratory

C. L. Wang

Linköpings universitet

L. Zhao

University at Buffalo, State University of New York

G. J. Biessels

University Medical Center Utrecht

M. A. Viergever

Image Sciences Institute

Computational Intelligence and Neuroscience

1687-5265 (ISSN) 1687-5273 (eISSN)

813696-

Ämneskategorier

Annan medicinteknik

Styrkeområden

Livsvetenskaper och teknik

DOI

10.1155/2015/813696