Automatic brain extraction and hemisphere segmentation in rat brain MR images after stroke using deformable models

被引:3
|
作者
Chang, Herng-Hua [1 ]
Yeh, Shin-Joe [2 ,3 ,4 ]
Chiang, Ming-Chang [5 ]
Hsieh, Sung-Tsang [2 ,3 ,4 ,6 ,7 ,8 ]
机构
[1] Natl Taiwan Univ, Dept Engn Sci & Ocean Engn, Computat Biomed Engn Lab CBEL, Taipei, Taiwan
[2] Natl Taiwan Univ, Grad Inst Anat & Cell Biol, Coll Med, Taipei 10051, Taiwan
[3] Natl Taiwan Univ Hosp, Dept Neurol, Taipei, Taiwan
[4] Natl Taiwan Univ Hosp, Stroke Ctr, Taipei, Taiwan
[5] Natl Yang Ming Chiao Tung Univ, Dept Biomed Engn, Taipei, Taiwan
[6] Natl Taiwan Univ, Grad Inst Clin Med, Coll Med, Taipei, Taiwan
[7] Natl Taiwan Univ, Grad Inst Brain & Mind Sci, Coll Med, Taipei, Taiwan
[8] Natl Taiwan Univ, Coll Med, Ctr Precis Med, Taipei, Taiwan
关键词
active contour model; DWI; level set; skull stripping; T2-weighted MRI; CEREBRAL HEMISPHERES; TISSUE SEGMENTATION; ISCHEMIC-STROKE; SURFACE; KURTOSIS; ATLAS;
D O I
10.1002/mp.15157
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose Experimental ischemic stroke models play an essential role in understanding the mechanisms of cerebral ischemia and evaluating the development of pathological extent. An important precursor to the investigation of ischemic strokes associated with rodents is the brain extraction and hemisphere segmentation in rat brain diffusion-weighted imaging (DWI) and T2-weighted MRI (T2WI) images. Accurate and reliable image segmentation tools for extracting the rat brain and hemispheres in the MR images are critical in subsequent processes, such as lesion identification and injury analysis. This study is an attempt to investigate rat brain extraction and hemisphere segmentation algorithms that are practicable in both DWI and T2WI images. Methods To automatically perform brain extraction, the proposed framework is based on an efficient geometric deformable model. By introducing an additional image force in response to the rat brain characteristics into the skull stripping model, we establish a unique rat brain extraction scheme in DWI and T2WI images. For the subsequent hemisphere segmentation, we develop an efficient brain feature detection algorithm to approximately separate the rat brain. A refinement process is enforced by constructing a gradient vector flow in the proximity of the midsurface, where a parametric active contour is attracted to achieve hemisphere segmentation. Results Extensive experiments with 55 DWI and T2WI subjects were executed in comparison with the state-of-the-art methods. Experimental results indicated that our rat brain extraction and hemisphere segmentation schemes outperformed the competitive methods and exhibited high performance both qualitatively and quantitatively. For rat brain extraction, the average Dice scores were 97.13% and 97.42% in DWI and T2WI image volumes, respectively. Rat hemisphere segmentation results based on the Hausdorff distance metric revealed average values of 0.17 and 0.15 mm for DWI and T2WI subjects, respectively. Conclusions We believe that the established frameworks are advantageous to facilitate preclinical stroke investigation and relevant neuroscience research that requires accurate brain extraction and hemisphere segmentation using rat DWI and T2WI images.
引用
收藏
页码:6036 / 6050
页数:15
相关论文
共 50 条
  • [1] A fast stochastic framework for automatic MR brain images segmentation
    Ismail, Marwa
    Soliman, Ahmed
    Ghazal, Mohammed
    Switala, Andrew E.
    Gimel'farb, Georgy
    Barnes, Gregory N.
    Khalil, Ashraf
    El-Baz, Ayman
    PLOS ONE, 2017, 12 (11):
  • [2] A Level Set Based Deformable Model for Segmentation of Human Brain MR Images
    Su, Chien-Ming
    Chang, Herng-Hua
    2014 7TH INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS (BMEI 2014), 2014, : 105 - 109
  • [3] Automatic segmentation of MR brain images of preterm infants using supervised classification
    Moeskops, Pim
    Benders, Manon J. N. L.
    Chita, SabinaM.
    Kersbergen, Karina J.
    Groenendaal, Floris
    de Vries, Linda S.
    Viergever, Max A.
    Isgum, Ivana
    NEUROIMAGE, 2015, 118 : 628 - 641
  • [4] Infarct Region Segmentation in Rat Brain T2 MR Images After Stroke Based on Fully Convolutional Networks
    Chang, Herng-Hua
    Yeh, Shin-Joe
    Chiang, Ming-Chang
    Hsieh, Sung-Tsang
    MEDICAL IMAGING 2020: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING, 2021, 11317
  • [5] Automatic segmentation of brain infarction in diffusion weighted MR images
    Li, W
    Tian, J
    MEDICAL IMAGING 2003: IMAGE PROCESSING, PTS 1-3, 2003, 5032 : 1531 - 1542
  • [6] Automatic Segmentation of Non-tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks
    Liu, Zhongqiang
    Gu, Dongdong
    Zhang, Yu
    Cao, Xiaohuan
    Xue, Zhong
    BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES (BRAINLES 2020), PT I, 2021, 12658 : 41 - 50
  • [7] Automatic Segmentation of Prostate from Multiparametric MR Images Using Hidden Features and Deformable Model
    Kharote, Prashant Ramesh
    Sankhe, Manoj S.
    Patkar, Deepak
    PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY, 2019, : 338 - 343
  • [8] Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification
    Hung-Ting Liu
    Tony W. H. Sheu
    Herng-Hua Chang
    Medical & Biological Engineering & Computing, 2013, 51 : 1091 - 1104
  • [9] Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification
    Liu, Hung-Ting
    Sheu, Tony W. H.
    Chang, Herng-Hua
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2013, 51 (10) : 1091 - 1104
  • [10] RU-Net: skull stripping in rat brain MR images after ischemic stroke with rat U-Net
    Chang, Herng-Hua
    Yeh, Shin-Joe
    Chiang, Ming-Chang
    Hsieh, Sung-Tsang
    BMC MEDICAL IMAGING, 2023, 23 (01)