Computational NeuroscienceAutomatic segmentation of the lateral geniculate nucleus: Application to control and glaucoma patients

- We provide an automatic LGN segmentation method, which is objective, efficient, valid and applicable.
- We find the LGN asymmetry and LGN atrophy along with the human age.
- We find that the bilateral LGN volumes shrinks in glaucoma patients and the LGN volumes are correlated with clinical parameters in patients.

BackgroundThe 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.New methodTo 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.ResultsThe 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.Comparison with existing methodsThe automatic LGN segmentation is objective, efficient, valid and applicable.ConclusionsExperiment 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.