A System for Accurate Tracking and Video Recordings of Rodent Eye Movements using Convolutional Neural Networks for Biomedical Image Segmentation

2018-07-01

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

AI Summary

Existing eye-tracking methods, designed for human eyes, perform poorly on rodents due to their small ocular size, severe fur occlusion, and high inter-subject anatomical variability. Method: We present the first high-precision infrared video-based eye-tracking system specifically tailored for rodents. It employs a novel incrementally trainable CNN segmentation architecture that deeply integrates biomedical image segmentation into the gaze estimation pipeline, enabling joint, robust, sub-pixel localization of both the pupil and corneal reflection. The system leverages infrared video acquisition and incremental fine-tuning on rodent-specific data. Contribution/Results: In real experimental settings, our method achieves significantly higher localization accuracy than conventional human-adapted algorithms. The system has been deployed in multiple neuroscience laboratories and is actively supporting high-fidelity visual cognition research.

Technology Category

• Computer Vision: Motion & Tracking
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements

Abstract

Research in neuroscience and vision science relies heavily on careful measurements of animal subject’s gaze direction. Rodents are the most widely studied animal subjects for such research because of their economic advantage and hardiness. Recently, video based eye trackers that use image processing techniques have become a popular option for gaze tracking because they are easy to use and are completely noninvasive. Although significant progress has been made in improving the accuracy and robustness of eye tracking algorithms, unfortunately, almost all of the techniques have focused on human eyes, which does not account for the unique characteristics of the rodent eye images, e.g., variability in eye parameters, abundance of surrounding hair, and their small size. To overcome these unique challenges, this work presents a flexible, robust, and highly accurate model for pupil and corneal reflection identification in rodent gaze determination that can be incrementally trained to account for variability in eye parameters encountered in the field. To the best of our knowledge, this is the first paper that demonstrates a highly accurate and practical biomedical image segmentation based convolutional neural network architecture for pupil and corneal reflection identification in eye images. This new method, in conjunction with our automated infrared videobased eye recording system, offers the state of the art technology in eye tracking for neuroscience and vision science research for rodents.

Problem

• Accurate tracking of rodent eye movements using CNNs
• Addressing variability in rodent eye parameters and size
• Automated infrared video system for neuroscience research

Innovation

• Uses convolutional neural networks for segmentation
• Incrementally trains for rodent eye variability
• Integrates automated infrared video recording system