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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

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Published on: December 19, 2020

Automatic segmentation of pulmonary lobes on low-dose computed tomography using deep learning.

Zewei Zhang¹, Jialiang Ren², Xiuli Tao¹

¹PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Annals of Translational Medicine

|March 12, 2021

Summary

This study developed an automated deep learning algorithm for pulmonary lobe segmentation on low-dose computed tomography (LDCT) images. The model accurately segments lobes efficiently, aiding lung cancer screening by improving nodule detection.

Keywords:

Computer-assisted image processing cancer screening computed tomography (CT)deep learning neural networks (computer)

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Pulmonary lobe segmentation is crucial for analyzing lung diseases.
Accurate segmentation on low-dose computed tomography (LDCT) images remains challenging.
Automated methods are needed to improve efficiency and consistency.

Purpose of the Study:

To develop and validate a deep learning algorithm for automated pulmonary lobe segmentation.
To assess the algorithm's performance on LDCT images for lung cancer screening.
To provide a tool for enhanced pulmonary nodule detection.

Main Methods:

A fully convolutional neural network, DenseVNet, was employed for segmentation.
The model was trained on 100 LDCT cases and validated on 10.
Performance was evaluated using Dice, Jaccard coefficients, and Hausdorff distance on 50 test cases.

Main Results:

The automated segmentation algorithm achieved high accuracy.
The all-lobes Dice coefficient was 0.944, Jaccard coefficient 0.896.
Segmentation was completed rapidly and without researcher intervention.

Conclusions:

The deep learning model offers robust and efficient pulmonary lobe segmentation from LDCT.
This automated approach can provide valuable location information for pulmonary nodule detection.
The method shows potential for clinical application in lung cancer screening.