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

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O-Net: An Overall Convolutional Network for Segmentation Tasks.

Omid Haji Maghsoudi¹, Aimilia Gastounioti¹, Lauren Pantalone¹

¹Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Machine Learning in Medical Imaging. MLMI (Workshop)

|July 20, 2021

Summary

A new Overall Convolutional Network (O-Net) improves medical image segmentation. This convolutional neural network (CNN) architecture enhances diagnostic accuracy by better capturing context in biomedical images.

Keywords:

Biomedical imaging Deep learning Segmentation

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Convolutional Neural Networks (CNNs) are widely used for image classification and segmentation.
Biomedical applications benefit significantly from improved segmentation accuracy for diagnosis and treatment planning.

Purpose of the Study:

Introduce a novel CNN architecture, the Overall Convolutional Network (O-Net).
Enhance the extraction of local and global contextual information for improved image segmentation.

Main Methods:

Developed the O-Net architecture utilizing diverse pooling levels and convolutional layers.
Evaluated O-Net performance on 2D biomedical image datasets.
Compared O-Net against U-Net and Pyramid Scene Parsing Net.
Investigated the impact of different encoders (simple, VGG Net, ResNet) on performance and stability.

Main Results:

O-Net achieved a higher dice coefficient compared to U-Net and Pyramid Scene Parsing Net.
The ResNet encoder generally improved segmentation results.
Analysis indicated robust model performance across training and validation sets.

Conclusions:

The O-Net architecture demonstrates superior performance in biomedical image segmentation.
O-Net's ability to integrate multi-level contextual information is key to its effectiveness.
Further research can explore O-Net's potential in diverse clinical applications.