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Gross Anatomy of the Lungs

Gross Anatomy of the Lungs

The lungs are a pair of vital organs connected to the trachea via the left and right bronchi. The base of these organs meets the dome-shaped muscle known as the diaphragm. Encased by the pleurae, the lungs contact the mediastinum. The right lung is shorter yet wider, and has a larger volume than the left lung. The left lung has an indentation known as the cardiac notch. The superior region of the lungs is referred to as the apex, whereas the base is the lower region near the diaphragm. The...

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Updated: Oct 18, 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

Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound With Applications to COVID-19.

Oz Frank, Nir Schipper, Mordehay Vaturi

IEEE Transactions on Medical Imaging

|October 4, 2021

Summary

This study introduces a deep learning framework to interpret lung ultrasound (LUS) images, integrating anatomical features and artifacts. This approach enhances the accuracy of LUS analysis for conditions like COVID-19 severity assessment.

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Point-of-Care Lung Ultrasound in Adults: Image Acquisition

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Point-of-Care Lung Ultrasound in Adults: Image Acquisition

Point-of-Care Lung Ultrasound in Adults: Image Acquisition

Published on: March 3, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonology

Background:

Lung ultrasound (LUS) is a safe, non-invasive bedside imaging tool.
Widespread adoption of LUS is limited by the need for expert interpretation.
Deep learning offers potential for automated LUS analysis.

Purpose of the Study:

To develop a framework for training deep neural networks to interpret LUS images.
To integrate domain knowledge (anatomical features, sonographic artifacts) into neural networks for LUS analysis.
To improve the efficiency and accuracy of LUS interpretation for clinical tasks.

Main Methods:

Proposed a framework integrating raw LUS frames with additional channels for pleural and vertical artifact masks.
Utilized domain knowledge to finetune standard off-the-shelf neural networks.
Applied the framework to COVID-19 severity assessment using the ICLUS dataset for frame classification and semantic segmentation.

Main Results:

The framework enabled rapid and efficient finetuning of standard neural networks for LUS tasks.
Models using raw LUS frames combined with artifact masks outperformed specialized models.
Achieved superior performance in COVID-19 severity assessment tasks (classification and segmentation).

Conclusions:

The proposed framework effectively integrates domain knowledge into deep learning models for LUS interpretation.
This approach enhances the performance of standard neural networks on LUS data.
The framework shows promise for broader adoption and application of LUS in clinical settings, including COVID-19 assessment.