Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks

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Standard

Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks. / Sigurdsson, Björn; Darkner, Sune; Sommer, Stefan Horst; Mortensen, Kristian Nygaard; Sanggaard, Simon; Kostrikov, Serhii; Nedergaard, Maiken.

2018. Abstract fra Joint Annual Meeting ISMRM-ESMRMB 2018, Paris, Frankrig.

Publikation: KonferencebidragKonferenceabstrakt til konferenceForskningfagfællebedømt

Harvard

Sigurdsson, B, Darkner, S, Sommer, SH, Mortensen, KN, Sanggaard, S, Kostrikov, S & Nedergaard, M 2018, 'Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks', Paris, Frankrig, 16/06/2018 - 21/10/2018, .

APA

Sigurdsson, B., Darkner, S., Sommer, S. H., Mortensen, K. N., Sanggaard, S., Kostrikov, S., & Nedergaard, M. (2018). Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks. Abstract fra Joint Annual Meeting ISMRM-ESMRMB 2018, Paris, Frankrig.

Vancouver

Sigurdsson B, Darkner S, Sommer SH, Mortensen KN, Sanggaard S, Kostrikov S o.a.. Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks. 2018. Abstract fra Joint Annual Meeting ISMRM-ESMRMB 2018, Paris, Frankrig.

Author

Sigurdsson, Björn ; Darkner, Sune ; Sommer, Stefan Horst ; Mortensen, Kristian Nygaard ; Sanggaard, Simon ; Kostrikov, Serhii ; Nedergaard, Maiken. / Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks. Abstract fra Joint Annual Meeting ISMRM-ESMRMB 2018, Paris, Frankrig.

Bibtex

@conference{c40f4bf218a347d7827a9989919e2c32,
title = "Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks",
abstract = "This study compares two different methods for the task of brain segmentation in rodent MR-images, a convolutional neural network (CNN) and majority voting of a registration based atlas (RBA) , and how limited training data affect their performance. The CNN was implemented in Tensorflow.The RBA performs better on average when using a training set with fewer than 20 images but the CNN achieves a higher median dice-score with a training set of 19 images.",
author = "Bj{\"o}rn Sigurdsson and Sune Darkner and Sommer, {Stefan Horst} and Mortensen, {Kristian Nygaard} and Simon Sanggaard and Serhii Kostrikov and Maiken Nedergaard",
year = "2018",
language = "English",
note = "Joint Annual Meeting ISMRM-ESMRMB 2018 ; Conference date: 16-06-2018 Through 21-10-2018",

}

RIS

TY - ABST

T1 - Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks

AU - Sigurdsson, Björn

AU - Darkner, Sune

AU - Sommer, Stefan Horst

AU - Mortensen, Kristian Nygaard

AU - Sanggaard, Simon

AU - Kostrikov, Serhii

AU - Nedergaard, Maiken

PY - 2018

Y1 - 2018

N2 - This study compares two different methods for the task of brain segmentation in rodent MR-images, a convolutional neural network (CNN) and majority voting of a registration based atlas (RBA) , and how limited training data affect their performance. The CNN was implemented in Tensorflow.The RBA performs better on average when using a training set with fewer than 20 images but the CNN achieves a higher median dice-score with a training set of 19 images.

AB - This study compares two different methods for the task of brain segmentation in rodent MR-images, a convolutional neural network (CNN) and majority voting of a registration based atlas (RBA) , and how limited training data affect their performance. The CNN was implemented in Tensorflow.The RBA performs better on average when using a training set with fewer than 20 images but the CNN achieves a higher median dice-score with a training set of 19 images.

M3 - Conference abstract for conference

ER -

ID: 204115825