A histological and diceCT-derived 3D reconstruction of the avian visual thalamofugal pathway

Abstract

Amniotes feature two principal visual processing systems: the tectofugal and thalamofugal pathways. In most mammals, the thalamofugal pathway predominates, routing retinal afferents through the dorsolateral geniculate complex to the visual cortex. In most birds, the thalamofugal pathway often plays the lesser role with retinal afferents projecting to the principal optic thalami, a complex of several nuclei that resides in the dorsal thalamus. This thalamic complex sends projections to a forebrain structure called the Wulst, the terminus of the thalamofugal visual system. The thalamofugal pathway in birds serves many functions such as pattern discrimination, spatial memory, and navigation/migration. A comprehensive analysis of avian species has unveiled diverse subdivisions within the thalamic and forebrain structures, contingent on species, age, and techniques utilized. In this study, we documented the thalamofugal system in three dimensions by integrating histological and contrast-enhanced computed tomography imaging of the avian brain. Sections of two-week-old chick brains were cut in either coronal, sagittal, or horizontal planes and stained with Nissl and either Gallyas silver or Luxol Fast Blue. The thalamic principal optic complex and pallial Wulst were subdivided on the basis of cell and fiber density. Additionally, we utilized the technique of diffusible iodine-based contrast-enhanced computed tomography (diceCT) on a 5-week-old chick brain, and right eyeball. By merging diceCT data, stained histological sections, and information from the existing literature, a comprehensive three-dimensional model of the avian thalamofugal pathway was constructed. The use of a 3D model provides a clearer understanding of the structural and spatial organization of the thalamofugal system. The ability to integrate histochemical sections with diceCT 3D modeling is critical to better understanding the anatomical and physiologic organization of complex pathways such as the thalamofugal visual system.

A multi-scale covariance matrix descriptor and an accurate transformation estimation for robust point cloud registration

“…where 𝐶 𝑀 1 and 𝐶 𝑀 2 correspond to two covariance matrix descriptors relating to different radius scales ranging from 𝑟 1 to 𝑟 𝑠 , and…”

“…More and more fields, including 3D reconstruction [1,2], simultaneous localization and mapping (SLAM) [3], autonomous driving [4], virtual reality [5], and others, have used point clouds obtained by laser scanning or stereo matching of images.…”

“…The aforementioned tasks may be hindered by the following significant obstacles. (1) Noise and outliers: Random fluctuation data close to the surface is referred to as noise, whereas frequent and inevitable points distant from the surface are referred to as outliers. (2) Various occlusion: Within the scanning device's field of view, Various occlusion is also inevitable and will result in point loss, which will impact the consistency of keypoints and the dependability of identified geometric characteristics.…”