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Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Related Experiment Video

Updated: May 24, 2025

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions

DTI of the Visual Pathway - White Matter Tracts and Cerebral Lesions

Published on: August 26, 2014

Dual-Uncertainty Guided Multimodal MRI-Based Visual Pathway Extraction.

Alou Diakite, Cheng Li, Yousuf Babiker M Osman

IEEE Transactions on Bio-Medical Engineering

|March 3, 2025

Summary

This summary is machine-generated.

This study introduces a novel method for extracting the visual pathway (VP) from MRI scans, significantly reducing the need for manual data labeling. The approach enhances extraction accuracy while minimizing annotation efforts, improving brain structure analysis.

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Area of Science:

Neuroimaging
Medical Image Analysis
Computational Neuroscience

Background:

Accurate extraction of the visual pathway (VP) from multimodal MRI is crucial for understanding brain structure and function.
Current methods often require extensive manual annotation, posing a significant bottleneck in research and clinical applications.

Purpose of the Study:

To develop a label-efficient approach for accurate visual pathway extraction from multimodal MRI.
To minimize reliance on large labeled datasets while enhancing extraction performance.

Main Methods:

Proposed a novel method incorporating a Modality-Relevant Feature Extraction Module (MRFEM) for T1-weighted and fractional anisotropy (FA) images.
Implemented a mean-teacher model with dual uncertainty-aware ambiguity identification (DUAI) to improve VP extraction reliability.

Main Results:

Demonstrated significant reduction in annotation efforts (at least one-third) compared to fully supervised methods on HCP and MDM datasets.
Achieved superior extraction performance over six state-of-the-art semi-supervised methods.

Conclusions:

The proposed label-efficient approach effectively reduces manual annotation burdens.
Enhanced accuracy in multimodal MRI-based VP extraction facilitates improved analysis of complex brain structures.