Random walk based optic chiasm localization using multi-parametric MRI for patients with pituitary adenoma

Min Sun; Zhiqiang Zhang M.D.; Chiyuan Ma M.D.; Suihua Chen; Xinjian Chen

doi:10.1117/12.2292426

2 March 2018 Random walk based optic chiasm localization using multi-parametric MRI for patients with pituitary adenoma

Min Sun, Zhiqiang Zhang M.D., Chiyuan Ma M.D., Suihua Chen, Xinjian Chen

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105743A (2018) https://doi.org/10.1117/12.2292426
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

The relative position between the optic chiasm and the pituitary adenoma will affect the pattern and severity of visual field defect, which is the most common and early onset visual disability induced by this kind of tumor. In this paper we describe an interactive method to localize the optic chiasm from multi-parametric magnetic resonance imaging (MRI) data by using a combined random walk algorithm. In the optic chiasm extraction framework, the modified random walk segmentation integrates the different information of T1-weighted (T1W) and T2-weighted (T2W) three-dimension (3-D) MRI data into the energy formulation to deduce the probabilities that voxels are assigned to the foreground and background. To avoid extract the wrong region into the object, we design a threshold based region detection method to segment the optic chiasm from the probabilities map. The proposed method is tested on 16 T1W and T2W MRI data from 16 patients diagnosed with pituitary adenoma. Experimental results show that the proposed method provides clinicians with good effectiveness and accuracy in the segmentation of the optic chiasm in patients with pituitary tumors to assist diagnosis and treatment.

Citation Download Citation

Min Sun, Zhiqiang Zhang M.D., Chiyuan Ma M.D., Suihua Chen, and Xinjian Chen "Random walk based optic chiasm localization using multi-parametric MRI for patients with pituitary adenoma", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105743A (2 March 2018); https://doi.org/10.1117/12.2292426

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KEYWORDS

Magnetic resonance imaging

Image segmentation

Visualization

Visual optics

Tumors

Expectation maximization algorithms

Optic nerve

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