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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Deep learning framework for bovine iris segmentation.

Heemoon Yoon¹, Mira Park¹, Hayoung Lee²

¹School of Information Communication and Technology, University of Tasmania, Hobart 7005, Australia.

Journal of Animal Science and Technology

|April 15, 2024

Summary

This summary is machine-generated.

This study introduces a deep learning framework for bovine iris segmentation, optimizing animal biometrics for livestock traceability. The U-Net with VGG16 backbone achieved 99.50% accuracy, even with minimal annotated data.

Keywords:

Cow Deep learning Identification Iris Segmentation

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Area of Science:

Computer Vision
Machine Learning
Animal Biometrics

Background:

Iris segmentation is crucial for livestock traceability systems.
Accurate animal identification relies on robust biometric data.

Purpose of the Study:

To develop a deep learning framework for pixel-wise bovine iris segmentation.
To optimize segmentation performance using minimal annotation labels.
To establish a reliable deep neural network (DNN) training framework.

Main Methods:

Utilized the BovineAAEyes80 public dataset.
Trained 15 deep neural network (DNN) models with varied encoder backbones and segmentation decoders.
Evaluated models using accuracy, dice coefficient, and graphical results.

Main Results:

Identified U-Net with a VGG16 backbone as the optimal model.
Achieved 99.50% accuracy and 98.35% dice coefficient score.
Demonstrated accurate segmentation of corrupted images with limited annotations.

Conclusions:

The proposed framework effectively segments bovine irises with high accuracy.
The U-Net with VGG16 backbone is optimal for this task.
This advancement supports reliable DNN training for livestock biometrics.