# Semi-Automated Lung Segmentation Based on Region-Growing Methods in Interstitial Lung Disease

## TL;DR

This paper introduces a semi-automated method for segmenting lung regions in CT scans, balancing automation and user input to improve accuracy in diagnosing pulmonary diseases.

## Contribution

A novel semi-automated lung segmentation technique using region-growing algorithms that reduces manual effort and computational complexity.

## Key findings

- The proposed method effectively delineates lung boundaries in CT scans.
- It balances automation with user control to improve segmentation accuracy.
- The approach is suitable for pulmonary disease diagnosis with reduced manual effort.

## Abstract

Background: One of the main tools for investigating pulmonary disorders is computed tomography. Starting with a CT, analyses can be qualitative (e.g., direct interpretation of 2D slices, virtual bronchoscopy) or quantitative (e.g., fibrosis score). Qualitative analyses can be performed without segmentation, but quantitative analyses require lung segmentation. Methods: We present the concepts for a class of lung segmentation methods that use region-growing algorithms, the implementation and testing details, and the results obtained in our software platform. Accurate segmentation of lung regions from medical images is a crucial step in computer-aided diagnosis (CAD) systems for pulmonary diseases such as chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer. Manual segmentation is time-consuming and subjective, while fully automated methods may fail under challenging imaging conditions. Results: This article presents a semi-automated lung segmentation approach, based on region-growing methods, that balances automation with user control. Conclusions: The proposed technique effectively delineates lung boundaries in computed tomography (CT), minimizing computational complexity and manual effort.