Automatic segmentation of the lateral geniculate nucleus: Application to control and glaucoma patients.
BACKGROUND
The lateral geniculate nucleus (LGN) is a key relay center of the visual system. Because the LGN morphology is affected by different diseases, it is of interest to analyze its morphology by segmentation. However, existing LGN segmentation methods are non-automatic, inefficient and prone to experimenters' bias.


METHODS
To address these problems, we proposed an automatic LGN segmentation algorithm based on T1-weighted imaging. First, the prior information of LGN was used to create a prior mask. Then region growing was applied to delineate LGN. We evaluated this automatic LGN segmentation method by (1) comparison with manually segmented LGN, (2) anatomically locating LGN in the visual system via LGN-based tractography, (3) application to control and glaucoma patients.


RESULTS
The similarity coefficients of automatic segmented LGN and manually segmented one are 0.72 (0.06) for the left LGN and 0.77 (0.07) for the right LGN. LGN-based tractography shows the subcortical pathway seeding from LGN passes the optic tract and also reaches V1 through the optic radiation, which is consistent with the LGN location in the visual system. In addition, LGN asymmetry as well as LGN atrophy along with age is observed in normal controls. The investigation of glaucoma effects on LGN volumes demonstrates that the bilateral LGN volumes shrink in patients.


METHODS
The automatic LGN segmentation is objective, efficient, valid and applicable.


CONCLUSIONS
Experiment results proved the validity and applicability of the algorithm. Our method will speed up the research on visual system and greatly enhance studies of different vision-related diseases.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 10090, China; Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing 10090, China. Electronic address: jieqiong.wang@ia.ac.cn.
Jieqiong
J
Wang
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 10090, China; Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing 10090, China. Electronic address: mwinvent@gmail.com.
Wen
W
Miao
Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China. Electronic address: lijingxbhtr@163.Com.
Jing
J
Li
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 10090, China; Department of Neurology, Otto-von-Guericke University, Germany. Electronic address: albertleemon@gmail.com.
Meng
M
Li
National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China. Electronic address: zhenzonglei@Qq.Com.
Zonglei
Z
Zhen
Otto-von-Guericke University of Magdeburg, Medical Faculty, Institute of Medical Psychology, Magdeburg, Germany. Electronic address: bernhard.Sabel@Med.Ovgu.De.
Bernhard
B
Sabel
Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China. Electronic address: cjr.Xianjunfang@Vip.163.Com.
Junfang
J
Xian
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 10090, China; Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing 10090, China. Electronic address: huiguang.he@ia.ac.cn.
Huiguang
H
He
D022126:Access to Information; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology; D000465:Algorithms; D001284:Atrophy; D016208:Databases, Factual; D005260:Female; D007839:Functional Laterality; D005829:Geniculate Bodies / Q000033:anatomy & histology* / Q000473:pathology; D005901:Glaucoma / Q000473:pathology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods*; D055815:Young Adult
Automatic segmentation; LGN; Structural MRI; Vision