Multi-modal image fusion based anatomical shape model for low-contrast anterior visual pathway and medial rectus muscle segmentation in CT Images

Segmentation of the anterior visual pathway (AVP, including optic nerve, optic chiasma, and optic tract) and the medial rectus muscle (MRM) in computed tomography (CT) images is indispensable in the treatment planning of skull base tumors. However, the optic tract and the optic chiasma are difficult to visualize because of weak CT imaging on soft tissue. In this paper, we propose a multi-modal image fusion-based segmentation approach for low-contrast AVP and MRM in CT images. First, a statistical shape model is constructed from the magnetic resonance (MR) images in which the optic tract and the optic chiasma are imaged clearly. Second, a deformation field is calculated by fusing the target CT image and the reference MR image and applied to the statistical shape as the initial segmentation result. Third, a multi-feature constrained surface of the AVP and the MRM is generated from the CT image. After fitting the initial segmentation result to the surface, the structures, including the optic tract and the optic chiasma that are invisible in CT images, can also be segmented. The proposed method is demonstrated on the clinical data of human head with respect to the Dice value and mean distance. The mean Dice and the mean distance between the segmentation results and the ground truth are 0.66 and 0.58, respectively.