Automatic detection of hippocampal atrophy on magnetic resonance images

被引:36
|
作者
Webb, J [1 ]
Guimond, A [1 ]
Eldridge, P [1 ]
Chadwick, D [1 ]
Meunier, J [1 ]
Thirion, JP [1 ]
Roberts, N [1 ]
机构
[1] Univ Liverpool, Magnet Resonance & Image Anal Res Ctr, Liverpool L69 3BX, Merseyside, England
关键词
atrophy; brain; hippocampus; image analysis; image registration; surgery; stereology; temporal lobe epilepsy;
D O I
10.1016/S0730-725X(99)00044-2
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
An automatic method for identifying hippocampal atrophy on magnetic resonance (MR) images obtained from patients with clinical evidence of temporal lobe epilepsy (TLE) is described, The method is based on the analysis of image intensity differences between patients and controls within a volume of interest (VOI) centred on the hippocampus. The core of the method is a fully automatic signal intensity-based inter-subject image registration technique. In particular, a global affine registration to a reference image is performed, followed by a local affine registration within the VOI, A mask produced by manual segmentation of the mean hippocampus for 30 control subjects enabled investigations to be restricted to a specified region of the VOI approximately corresponding to the hippocampus. Normal variations of hippocampal signal intensity were computed from images obtained for the 30 control subjects. The manual method of hippocampal volumetry, currently an important component of the pre-surgical evaluation of patients with clinical evidence of medically intractable TLE, is used to determine the lower 1(st) percentile limits of normal hippocampal volume. Hippocampi with volumes below this limit are defined as atrophic, We investigated whether the automatic method can correctly distinguish between 15 patients with significant hippocampal atrophy according to absolute volumes and a further 14 controls. ROC curves enabled evaluation of sensitivity and specificity in respect of an intensity threshold, 100% specificity is required when determining suitability of patients for neurosurgery) resulting in levels of 50% and 70% sensitivity in detecting atrophy in the right and left hippocampus, respectively, We propose that the method can be developed as an automatic screening procedure. (C) 1999 Elsevier Science Inc.
引用
收藏
页码:1149 / 1161
页数:13
相关论文
共 50 条
  • [31] Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery
    Jodas, Danilo Samuel
    Pereira, Aledir Silveira
    Tavares, Joao Manuel R. S.
    VIPIMAGE 2017, 2018, 27 : 92 - 101
  • [32] The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
    Liang, Yao-Wen
    Fang, Yu-Ting
    Lin, Ting-Chun
    Yang, Cheng-Ru
    Chang, Chih-Chang
    Chang, Hsuan-Kan
    Ko, Chin-Chu
    Tu, Tsung-Hsi
    Fay, Li-Yu
    Wu, Jau-Ching
    Huang, Wen-Cheng
    Hu, Hsiang-Wei
    Chen, You-Yin
    Kuo, Chao-Hung
    NEUROSPINE, 2024, 21 (02) : 665 - 675
  • [33] Automatic frequency correction for quantification of magnetic resonance spectroscopic images
    Wang, Y
    Van Huffel, S
    Heyvaert, E
    Vanhamme, L
    Van Hecke, P
    MATHEMATICS IN SIGNAL PROCESSING V, 2002, (71): : 229 - 239
  • [34] An algorithm for automatic segmentation and classification of magnetic resonance brain images
    Bradley J. Erickson
    Ramesh T. V. Avula
    Journal of Digital Imaging, 1998, 11
  • [35] A multiresolution prostate representation for automatic segmentation in magnetic resonance images
    Alvarez, Charlens
    Martinez, Fabio
    Romero, Eduardo
    MEDICAL PHYSICS, 2017, 44 (04) : 1312 - 1323
  • [36] Hippocampal layers on high resolution magnetic resonance images: real or imaginary?
    Wieshmann, UC
    Symms, MR
    Mottershead, JP
    Macmanus, DG
    Barker, GJ
    Tofts, PS
    Revesz, T
    Stevens, JM
    Shorvon, SD
    JOURNAL OF ANATOMY, 1999, 195 : 131 - 135
  • [37] A New Convolutional Neural Network Architecture for Automatic Detection of Brain Tumors in Magnetic Resonance Imaging Images
    Musallam, Ahmed S.
    Sherif, Ahmed S.
    Hussein, Mohamed K.
    IEEE ACCESS, 2022, 10 : 2775 - 2782
  • [38] Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy
    Wang, Rui
    Li, Chao
    Wang, Jie
    Wei, Xiaoer
    Li, Yuehua
    Hui, Chun
    Zhu, Yuemin
    Zhang, Su
    MAGNETIC RESONANCE IMAGING, 2014, 32 (10) : 1321 - 1329
  • [39] A cellular neural network based method for classification of magnetic resonance images:: Towards an automated detection of hippocampal sclerosis
    Doehler, Florian
    Mormann, Florian
    Weber, Bernd
    Elger, Christian E.
    Lehnertz, Klaus
    JOURNAL OF NEUROSCIENCE METHODS, 2008, 170 (02) : 324 - 331
  • [40] Automatic detection of peripapillary atrophy in retinal fundus images using statistical features
    Septiarini, Anindita
    Harjoko, Agus
    Pulungan, Reza
    Ekantini, Retno
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2018, 45 : 151 - 159