Hippocampus segmentation from MR infant brain images via boundary regression

Yeqin Shao, Yanrong Guo, Yaozong Gao, Xin Yang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Hippocampus segmentation from MR infant brain images is indispensable for studying early brain development. However, most of hippocampus segmentation methods were developed for adult brain images, which are not suitable for infant brain images of the first year due to low image contrast and variable structural patterns of early hippocampal development. To address these challenges, we propose a boundary regression method to detect hippocampal boundaries in the infant brain images, and then use the obtained boundaries to guide the deformable segmentation. The advantages of our segmentation method are: (1) different from the recently-developed atlas-based hippocampus segmentation methods, our method does not perform time-consuming deformable registrations; (2) different from the conventional point-regression-based boundary detection methods, our boundary regression method can predict the whole hippocampal boundary by a single regression model. Experiments on MR infant brain images from 2-week-old to 1-year-old show promising hippocampus segmentation results.

Original languageEnglish
Title of host publicationMedical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
PublisherSpringer Verlag
Pages146-154
Number of pages9
Volume9601
ISBN (Print)9783319420158
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany
Duration: 2015 Oct 92015 Oct 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9601
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
CountryGermany
CityGermany
Period15/10/915/10/9

Fingerprint

Hippocampus
Brain
Segmentation
Regression
Boundary Detection
Atlas
Registration
Regression Model
Predict

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shao, Y., Guo, Y., Gao, Y., Yang, X., & Shen, D. (2016). Hippocampus segmentation from MR infant brain images via boundary regression. In Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers (Vol. 9601, pp. 146-154). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601). Springer Verlag. https://doi.org/10.1007/978-3-319-42016-5_14

Hippocampus segmentation from MR infant brain images via boundary regression. / Shao, Yeqin; Guo, Yanrong; Gao, Yaozong; Yang, Xin; Shen, Dinggang.

Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Vol. 9601 Springer Verlag, 2016. p. 146-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shao, Y, Guo, Y, Gao, Y, Yang, X & Shen, D 2016, Hippocampus segmentation from MR infant brain images via boundary regression. in Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. vol. 9601, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9601, Springer Verlag, pp. 146-154, International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI, Germany, Germany, 15/10/9. https://doi.org/10.1007/978-3-319-42016-5_14
Shao Y, Guo Y, Gao Y, Yang X, Shen D. Hippocampus segmentation from MR infant brain images via boundary regression. In Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Vol. 9601. Springer Verlag. 2016. p. 146-154. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-42016-5_14
Shao, Yeqin ; Guo, Yanrong ; Gao, Yaozong ; Yang, Xin ; Shen, Dinggang. / Hippocampus segmentation from MR infant brain images via boundary regression. Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Vol. 9601 Springer Verlag, 2016. pp. 146-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{4fbeeea4358e49d99b798c7cc8b7aeff,
title = "Hippocampus segmentation from MR infant brain images via boundary regression",
abstract = "Hippocampus segmentation from MR infant brain images is indispensable for studying early brain development. However, most of hippocampus segmentation methods were developed for adult brain images, which are not suitable for infant brain images of the first year due to low image contrast and variable structural patterns of early hippocampal development. To address these challenges, we propose a boundary regression method to detect hippocampal boundaries in the infant brain images, and then use the obtained boundaries to guide the deformable segmentation. The advantages of our segmentation method are: (1) different from the recently-developed atlas-based hippocampus segmentation methods, our method does not perform time-consuming deformable registrations; (2) different from the conventional point-regression-based boundary detection methods, our boundary regression method can predict the whole hippocampal boundary by a single regression model. Experiments on MR infant brain images from 2-week-old to 1-year-old show promising hippocampus segmentation results.",
author = "Yeqin Shao and Yanrong Guo and Yaozong Gao and Xin Yang and Dinggang Shen",
year = "2016",
doi = "10.1007/978-3-319-42016-5_14",
language = "English",
isbn = "9783319420158",
volume = "9601",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "146--154",
booktitle = "Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers",

}

TY - GEN

T1 - Hippocampus segmentation from MR infant brain images via boundary regression

AU - Shao, Yeqin

AU - Guo, Yanrong

AU - Gao, Yaozong

AU - Yang, Xin

AU - Shen, Dinggang

PY - 2016

Y1 - 2016

N2 - Hippocampus segmentation from MR infant brain images is indispensable for studying early brain development. However, most of hippocampus segmentation methods were developed for adult brain images, which are not suitable for infant brain images of the first year due to low image contrast and variable structural patterns of early hippocampal development. To address these challenges, we propose a boundary regression method to detect hippocampal boundaries in the infant brain images, and then use the obtained boundaries to guide the deformable segmentation. The advantages of our segmentation method are: (1) different from the recently-developed atlas-based hippocampus segmentation methods, our method does not perform time-consuming deformable registrations; (2) different from the conventional point-regression-based boundary detection methods, our boundary regression method can predict the whole hippocampal boundary by a single regression model. Experiments on MR infant brain images from 2-week-old to 1-year-old show promising hippocampus segmentation results.

AB - Hippocampus segmentation from MR infant brain images is indispensable for studying early brain development. However, most of hippocampus segmentation methods were developed for adult brain images, which are not suitable for infant brain images of the first year due to low image contrast and variable structural patterns of early hippocampal development. To address these challenges, we propose a boundary regression method to detect hippocampal boundaries in the infant brain images, and then use the obtained boundaries to guide the deformable segmentation. The advantages of our segmentation method are: (1) different from the recently-developed atlas-based hippocampus segmentation methods, our method does not perform time-consuming deformable registrations; (2) different from the conventional point-regression-based boundary detection methods, our boundary regression method can predict the whole hippocampal boundary by a single regression model. Experiments on MR infant brain images from 2-week-old to 1-year-old show promising hippocampus segmentation results.

UR - http://www.scopus.com/inward/record.url?scp=84981316114&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84981316114&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-42016-5_14

DO - 10.1007/978-3-319-42016-5_14

M3 - Conference contribution

SN - 9783319420158

VL - 9601

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 146

EP - 154

BT - Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers

PB - Springer Verlag

ER -