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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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Pulmonary Fissure Segmentation in CT Images Using Image Filtering and Machine Learning.

Mikhail Fufin¹, Vladimir Makarov¹, Vadim I Alfimov¹

¹Medical Informatics Laboratory, Yaroslav-the-Wise Novgorod State University, 41 B. St. Petersburgskaya, Veliky Novgorod 173003, Russia.

Tomography (Ann Arbor, Mich.)

|October 25, 2024

Summary

This study presents an automated method for segmenting lung fissures and lobes using U-Net and PAN models. The approach significantly improves accuracy for fissure segmentation, aiding in lung disease diagnosis.

Keywords:

CNN computed tomography fissure lung machine learning segmentation stick derivative

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Published on: October 13, 2023

Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Pulmonary Medicine

Background:

Lung lobe and fissure segmentation are crucial for diagnosing and evaluating lung diseases.
Quantifying individual lung lobes is clinically significant as diseases often affect specific lobes.
Fissure segmentation is vital for many lobe segmentation techniques and assessing fissure integrity.

Purpose of the Study:

To develop a fully automatic method for pulmonary fissure segmentation on lung CT scans.
To improve the accuracy and efficiency of lung lobe and fissure segmentation for clinical applications.

Main Methods:

Utilized U-Net and PAN models for automatic pulmonary fissure segmentation.
Employed a Derivative of Stick (DoS) filter for data preprocessing.
Implemented model ensembling to enhance prediction accuracy.

Main Results:

Achieved high F1 scores for fissure segmentation: 0.916 (right lung) and 0.933 (left lung).
Demonstrated superior performance compared to standalone DoS filter results.
Lung lobe segmentation using the proposed method yielded an average Dice score of 0.989, comparable to state-of-the-art techniques.

Conclusions:

The developed method efficiently segments pulmonary fissures with low memory requirements.
The approach is suitable for rapid experimentation and further research in lung image analysis.
This automated segmentation technique can support clinical diagnosis and disease evaluation.