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Updated: Nov 28, 2025

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

Capsules for biomedical image segmentation.

Rodney LaLonde¹, Ziyue Xu², Ismail Irmakci³

¹Center for Research in Computer Vision (CRCV), University of Central Florida (UCF), Orlando, FL.

Medical Image Analysis

|November 27, 2020

Summary

This summary is machine-generated.

This study introduces SegCaps, a novel capsule network for object segmentation, achieving state-of-the-art results with significantly fewer parameters. The method excels in segmenting pathological lungs and human tissues, demonstrating broad applicability and generalization capabilities.

Keywords:

Capsule network Lung segmentation Pre-clinical imaging Thigh MRI segmentation

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Area of Science:

Computer Vision
Medical Image Analysis
Deep Learning

Background:

Capsule networks offer advantages over traditional convolutional neural networks for image analysis.
Existing segmentation methods often require substantial computational resources and parameters.

Purpose of the Study:

To adapt capsule networks for object segmentation tasks, addressing limitations of current approaches.
To develop an efficient and effective deep learning model for medical image segmentation.

Main Methods:

Introduced locally-constrained routing and transformation matrix sharing in capsule networks.
Developed SegCaps, a deep encoder-decoder network utilizing deconvolutional capsules.
Extended masked reconstruction regularization for segmentation tasks.

Main Results:

SegCaps achieved state-of-the-art performance in segmenting pathological lungs and human tissues (muscle/adipose).
The model utilized a fraction of the parameters compared to popular segmentation networks like U-Net (less than 5%).
Demonstrated generalization capabilities on natural images, handling rotations and reflections.

Conclusions:

SegCaps represents a significant advancement in capsule network applications for object segmentation.
The proposed method offers a highly efficient and accurate solution for medical image segmentation.
The approach shows promise for large-scale medical imaging studies and diverse segmentation challenges.