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Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN.

Ning Yue¹, Jingwei Zhang², Jing Zhao³

¹Department of Radiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, China.

Bioengineering (Basel, Switzerland)

|August 25, 2022

Summary

This study introduces a novel AI system for detecting and classifying bronchiectasis using low-dose CT (LDCT) scans. The system achieves high accuracy, aiding doctors in efficient diagnosis and reducing workload.

Keywords:

LDCT Mask R-CNN automated scoring bronchiectasis decision support system object detection

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Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonology

Background:

Bronchiectasis, a chronic lung condition, is diagnosed via CT scans, but current methods lack standardized severity classification.
Existing research often overlooks low-dose CT (LDCT) in favor of high-resolution CT (HRCT), limiting practical applications.
Improved accuracy in bronchiectasis detection and classification is crucial for effective patient management.

Purpose of the Study:

To develop an automated system for detecting and classifying bronchiectasis using LDCT images.
To establish a reliable method for assigning bronchiectasis severity scores based on established criteria.
To improve the efficiency and accuracy of bronchiectasis diagnosis in clinical practice.

Main Methods:

Proposed ACER image enhancement, RDU-Net lung lobe segmentation, and HDC Mask R-CNN models for bronchiectasis analysis.
Developed a Python-based system for automated LDCT image processing, detection, and classification.
Integrated Reiff and BRICS scoring criteria for automatic patient bronchiectasis scoring.

Main Results:

Achieved 91.4% accuracy in bronchiectasis detection and classification.
Reported IOU, sensitivity, and specificity of 88.8%, 88.6%, and 85.4%, respectively.
Demonstrated a rapid recognition speed of approximately 1 second per image.

Conclusions:

The developed AI system accurately detects and classifies bronchiectasis from LDCT scans, matching human doctor accuracy.
The system significantly reduces physician workload by efficiently processing large datasets and handling routine cases.
This automated approach provides a valuable tool for clinical decision-making in bronchiectasis diagnosis and management.