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Related Experiment Video

Updated: Dec 31, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Semi-Supervised Learning for Semantic Segmentation of Emphysema With Partial Annotations.

Liying Peng, Lanfen Lin, Hongjie Hu

IEEE Journal of Biomedical and Health Informatics

|January 7, 2020

Summary

This summary is machine-generated.

This study introduces a semi-supervised framework for segmenting emphysema in CT scans using partial annotations. The novel Fisher loss enhances model performance, outperforming existing methods for chronic obstructive pulmonary disease monitoring.

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Last Updated: Dec 31, 2025

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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

Area of Science:

Medical Imaging
Pulmonary Medicine
Artificial Intelligence

Background:

Accurate segmentation of emphysema subtypes is crucial for monitoring chronic obstructive pulmonary disease (COPD).
The diffuse nature of emphysema makes pixel-level semantic labeling in CT images challenging for experts.
Partial annotation strategies can reduce radiologist workload while maintaining segmentation utility.

Purpose of the Study:

To develop an end-to-end trainable semi-supervised framework for emphysema semantic segmentation using partial annotations.
To introduce and integrate a novel Fisher loss function to improve model discriminative power.
To evaluate the proposed framework's performance against supervised and state-of-the-art methods.

Main Methods:

Proposed a semi-supervised framework trained on both annotated and unannotated regions of CT images.
Introduced a novel Fisher loss function to enhance the discriminative capabilities of the segmentation model.
Integrated the Fisher loss into the end-to-end trainable framework for emphysema segmentation.

Main Results:

The proposed semi-supervised framework demonstrated superior performance compared to a baseline supervised approach using only annotated areas.
The method achieved state-of-the-art results for emphysema segmentation.
The Fisher loss effectively enhanced the model's discriminative power.

Conclusions:

The developed semi-supervised framework with Fisher loss offers an effective solution for emphysema segmentation with partial annotations.
This approach significantly improves segmentation accuracy and efficiency, aiding in COPD monitoring.
The findings suggest a promising direction for medical image analysis in pulmonary diseases.