A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Overview

Journal J Digit Imaging

Publisher Springer

Specialties Medical Informatics
Radiology

Date 2015 Jun 10

PMID 26055544

Citations 19

Authors

Sudipta Mukhopadhyay

Affiliations

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Abstract

Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The well-circumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/part-solid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

Citing Articles

Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.

Alhajim D, Ansari-Asl K, Akbarizadeh G, Soorki M Sci Rep. 2025; 15(1):3770.

PMID: 39885263 PMC: 11782676. DOI: 10.1038/s41598-025-85199-5.

A Survey on Artificial Intelligence in Pulmonary Imaging.

Saha P, Nadeem S, Comellas A Wiley Interdiscip Rev Data Min Knowl Discov. 2024; 13(6).

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Improved Complementary Pulmonary Nodule Segmentation Model Based on Multi-Feature Fusion.

Tang T, Li F, Jiang M, Xia X, Zhang R, Lin K Entropy (Basel). 2022; 24(12).

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Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation.

Bruntha P, Pandian S, Sagayam K, Bandopadhyay S, Pomplun M, Dang H Sci Rep. 2022; 12(1):20330.

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