Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 29, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Enhancing the Generalizability of Deep Learning-Based Models for Lung Field Segmentation in Chest Radiographs Using

Tairah Andrabi¹, Sajid Yousuf Bhat¹

¹Department of Computer Science, University of Kashmir, Srinagar, Jammu and Kashmir, India, kashmiruniversity.net.

International Journal of Biomedical Imaging

|May 11, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same journal

Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.

International journal of biomedical imaging·2026

Same journal

Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.

International journal of biomedical imaging·2026

Same journal

Generative AI-Driven CNN Framework for Enhanced Lung Cancer Detection, Prediction, and Treatment: A Novel Approach to Overcoming AI Limitations.

International journal of biomedical imaging·2026

Same journal

Personalized PET Imaging in Gastric Cancer: An Umbrella Review of Meta-Analyses to Guide Radiopharmaceutical Selection and Clinical Indication.

International journal of biomedical imaging·2026

Same journal

Clinician-Centric Explainable Artificial Intelligence Framework for Medical Imaging Diagnostics: A Systematic Review.

International journal of biomedical imaging·2026

Same journal

Digital Pathology-Based Comparison of PyRadiomics and HistomicsTK for Nuclei Classification in Melanoma Whole Slide Images.

International journal of biomedical imaging·2026

See all related articles

This study introduces a novel hybrid approach for precise lung field segmentation in chest X-rays, enhancing computer-assisted diagnosis. The method improves accuracy and generalizability across diverse datasets, aiding respiratory disease detection.

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Lung field segmentation (LFS) in chest X-rays (CXR) is crucial for diagnosing respiratory diseases.
Deep learning models struggle with precise LFS due to poor image contrast and overlapping structures, leading to poor generalizability.

Purpose of the Study:

To develop a robust and generalizable two-phase hybrid approach for accurate LFS in CXR.
To overcome limitations of existing deep learning models in handling image variations and complex anatomical structures.

Main Methods:

A two-phase hybrid method combining heuristic edge detection (Laplacian of Gaussian and Canny filters) with deep learning (U-Net architectures).
Phase 1: Extracting and refining multiscale edge features using LoG and Canny filters.

Keywords:

CXR image deep learning edge features feature fusion lung field segmentation morphology

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Related Experiment Videos

Last Updated: Jun 29, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Phase 2: Fusing enriched feature maps with original contrast inputs to train U-Net models.

Main Results:

The edge-assisted approach significantly improved LFS performance across three benchmark datasets (MC, SH, JSRT).
Deep attention U-Net achieved a Dice coefficient of 0.9815 and Jaccard score of 0.9624 on the JSRT dataset.
The method demonstrated improved Dice and IoU gains compared to baseline models and showed strong cross-dataset validation results.

Conclusions:

The proposed hybrid method offers a reliable and effective solution for LFS in diverse and challenging clinical settings.
Enhanced LFS accuracy and generalizability contribute to improved computer-assisted diagnosis and patient care for respiratory conditions.