CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans

TL;DR

This paper introduces CIDI-Lung-Seg, an interactive, cross-platform lung segmentation tool for CT scans that combines automatic fuzzy-connectedness segmentation with manual correction, aiming to improve clinical efficiency and accuracy.

Contribution

The paper presents a novel, user-friendly lung annotation tool that integrates automatic segmentation with manual correction, addressing limitations of existing methods in clinical practice.

Findings

Higher consistency and precision compared to commercial tools

Supports Windows, Linux, and Mac OS X

Potential to enhance routine clinical tasks

Abstract

Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies. Manual annotation, current gold standard, is time consuming and often subject to human bias. On the other hand, current state-of-the-art fully automated lung segmentation methods fail to make their way into the clinical practice due to their inability to efficiently incorporate human input for handling misclassifications and praxis. This paper presents a lung annotation tool for CT images that is interactive, efficient, and robust. The proposed annotation tool produces an "as accurate as possible" initial annotation based on the fuzzy-connectedness image segmentation, followed by efficient manual fixation of the initial extraction if deemed necessary by the practitioner. To provide maximum flexibility to the users, our annotation tool is supported in three major operating systems (Windows, Linux, and the Mac OS X). The quantitative results comparing our free software with commercially available lung segmentation tools show higher degree of consistency and precision of our software with a considerable potential to enhance the performance of routine clinical tasks.