Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery

Zhenyu Tang, Sahar Ahmad, Pew Thian Yap, Dinggang Shen

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

We introduce a new multi-atlas segmentation (MAS) framework for MR tumor brain images. The basic idea of MAS is to register and fuse label information from multiple normal brain atlases to a new brain image for segmentation. Many MAS methods have been proposed with success. However, most of them are developed for normal brain images, and tumor brain images usually pose a great challenge for them. This is because tumors cause difficulties in registration of normal brain atlases to the tumor brain image. To address this challenge, in the first step of our MAS framework, a new low-rank method is used to get the recovered image of normal-looking brain from the MR tumor brain image based on the information of normal brain atlases. Different from conventional low-rank methods that produce the recovered image with distorted normal brain regions, our low-rank method harnesses a spatial constraint to get the recovered image with preserved normal brain regions. Then in the second step, normal brain atlases can be registered to the recovered image without influence from tumors. These two steps are iteratively proceeded until convergence, for obtaining the final segmentation of the tumor brain image. During the iteration, both the recovered image and the registration of normal brain atlases to the recovered image are gradually refined. We have compared our proposed method with a state-of-the-art method by using both synthetic and real MR tumor brain images. Experimental results show that our proposed method can get effectively recovered images and also improves segmentation accuracy.

Original languageEnglish
JournalIEEE Transactions on Medical Imaging
DOIs
Publication statusAccepted/In press - 2018 Apr 6

Fingerprint

Atlases
Brain Neoplasms
Tumors
Brain
Recovery
Neoplasms
Electric fuses

Keywords

  • Brain
  • Convergence
  • image recovery
  • Image segmentation
  • Low-rank
  • multi-atlas segmentation
  • Pathology
  • Radiology
  • spatial constraint
  • Three-dimensional displays
  • tumor brain image
  • Tumors

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery. / Tang, Zhenyu; Ahmad, Sahar; Yap, Pew Thian; Shen, Dinggang.

In: IEEE Transactions on Medical Imaging, 06.04.2018.

Research output: Contribution to journalArticle

@article{463b77c4895840f0bc4b60659e5032d0,
title = "Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery",
abstract = "We introduce a new multi-atlas segmentation (MAS) framework for MR tumor brain images. The basic idea of MAS is to register and fuse label information from multiple normal brain atlases to a new brain image for segmentation. Many MAS methods have been proposed with success. However, most of them are developed for normal brain images, and tumor brain images usually pose a great challenge for them. This is because tumors cause difficulties in registration of normal brain atlases to the tumor brain image. To address this challenge, in the first step of our MAS framework, a new low-rank method is used to get the recovered image of normal-looking brain from the MR tumor brain image based on the information of normal brain atlases. Different from conventional low-rank methods that produce the recovered image with distorted normal brain regions, our low-rank method harnesses a spatial constraint to get the recovered image with preserved normal brain regions. Then in the second step, normal brain atlases can be registered to the recovered image without influence from tumors. These two steps are iteratively proceeded until convergence, for obtaining the final segmentation of the tumor brain image. During the iteration, both the recovered image and the registration of normal brain atlases to the recovered image are gradually refined. We have compared our proposed method with a state-of-the-art method by using both synthetic and real MR tumor brain images. Experimental results show that our proposed method can get effectively recovered images and also improves segmentation accuracy.",
keywords = "Brain, Convergence, image recovery, Image segmentation, Low-rank, multi-atlas segmentation, Pathology, Radiology, spatial constraint, Three-dimensional displays, tumor brain image, Tumors",
author = "Zhenyu Tang and Sahar Ahmad and Yap, {Pew Thian} and Dinggang Shen",
year = "2018",
month = "4",
day = "6",
doi = "10.1109/TMI.2018.2824243",
language = "English",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery

AU - Tang, Zhenyu

AU - Ahmad, Sahar

AU - Yap, Pew Thian

AU - Shen, Dinggang

PY - 2018/4/6

Y1 - 2018/4/6

N2 - We introduce a new multi-atlas segmentation (MAS) framework for MR tumor brain images. The basic idea of MAS is to register and fuse label information from multiple normal brain atlases to a new brain image for segmentation. Many MAS methods have been proposed with success. However, most of them are developed for normal brain images, and tumor brain images usually pose a great challenge for them. This is because tumors cause difficulties in registration of normal brain atlases to the tumor brain image. To address this challenge, in the first step of our MAS framework, a new low-rank method is used to get the recovered image of normal-looking brain from the MR tumor brain image based on the information of normal brain atlases. Different from conventional low-rank methods that produce the recovered image with distorted normal brain regions, our low-rank method harnesses a spatial constraint to get the recovered image with preserved normal brain regions. Then in the second step, normal brain atlases can be registered to the recovered image without influence from tumors. These two steps are iteratively proceeded until convergence, for obtaining the final segmentation of the tumor brain image. During the iteration, both the recovered image and the registration of normal brain atlases to the recovered image are gradually refined. We have compared our proposed method with a state-of-the-art method by using both synthetic and real MR tumor brain images. Experimental results show that our proposed method can get effectively recovered images and also improves segmentation accuracy.

AB - We introduce a new multi-atlas segmentation (MAS) framework for MR tumor brain images. The basic idea of MAS is to register and fuse label information from multiple normal brain atlases to a new brain image for segmentation. Many MAS methods have been proposed with success. However, most of them are developed for normal brain images, and tumor brain images usually pose a great challenge for them. This is because tumors cause difficulties in registration of normal brain atlases to the tumor brain image. To address this challenge, in the first step of our MAS framework, a new low-rank method is used to get the recovered image of normal-looking brain from the MR tumor brain image based on the information of normal brain atlases. Different from conventional low-rank methods that produce the recovered image with distorted normal brain regions, our low-rank method harnesses a spatial constraint to get the recovered image with preserved normal brain regions. Then in the second step, normal brain atlases can be registered to the recovered image without influence from tumors. These two steps are iteratively proceeded until convergence, for obtaining the final segmentation of the tumor brain image. During the iteration, both the recovered image and the registration of normal brain atlases to the recovered image are gradually refined. We have compared our proposed method with a state-of-the-art method by using both synthetic and real MR tumor brain images. Experimental results show that our proposed method can get effectively recovered images and also improves segmentation accuracy.

KW - Brain

KW - Convergence

KW - image recovery

KW - Image segmentation

KW - Low-rank

KW - multi-atlas segmentation

KW - Pathology

KW - Radiology

KW - spatial constraint

KW - Three-dimensional displays

KW - tumor brain image

KW - Tumors

UR - http://www.scopus.com/inward/record.url?scp=85045193735&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045193735&partnerID=8YFLogxK

U2 - 10.1109/TMI.2018.2824243

DO - 10.1109/TMI.2018.2824243

M3 - Article

C2 - 29993928

AN - SCOPUS:85045193735

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

SN - 0278-0062

ER -