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

Related Concept Videos

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological Investigation III: Pulmonary Angiogram and PET Scan

Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...

You might also read

Related Articles

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

Sort by

Same author

High rate of chimeric gene origination by retroposition in plant genomes.

The Plant cell·2006

Same author

Enzyme catalysis: tool to make and break amygdalin hydrogelators from renewable resources: a delivery model for hydrophobic drugs.

Journal of the American Chemical Society·2006

Same author

Theoretical probing of deltahedral closo-auroboranes B(x)Au(x)2- (x = 5-12).

Inorganic chemistry·2006

Same author

Density functional theory/time-dependent DFT studies on the structures, trend in DNA-binding affinities, and spectral properties of complexes [Ru(bpy)2(p-R-pip)]2+ (R = -OH, -CH3, -H, -NO2).

The journal of physical chemistry. A·2006

Same author

Sn12(2-): stannaspherene.

Journal of the American Chemical Society·2006

Same author

High efficient mammalian expression and secretion of a functional humanized single-chain Fv/human interleukin-2 molecules.

World journal of gastroenterology·2006

Same journal

Multi-Scale convolutional neural networks integrated with self-attention for motor imagery EEG decoding.

Biomedical engineering letters·2026

Same journal

Low-power analog and mixed-signal circuit techniques for next-generation miniature implantable neural interface systems.

Biomedical engineering letters·2026

Same journal

Advances in semiconductor materials and device architectures for biomedical systems: a mini review.

Biomedical engineering letters·2026

Same journal

A Multi-perception fusion using shared-control method for brain-mobile robot.

Biomedical engineering letters·2026

Same journal

SSA-DCNet: a cross-session MI-EEG classification network based on deformable convolution and spatial-shift attention.

Biomedical engineering letters·2026

Same journal

Advanced silicon nanomembrane based bioelectronics for flexible and stretchable implantable systems.

Biomedical engineering letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 16, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

A study for expert-informed active pulmonary nodule segmentation.

Shuangping Tan¹, Tong Zhang², Youfeng Deng³

¹Wuhan Puai Hospital, Tongji Medical College, Huazhong University of Science and Technology (Wuhan Fourth Hospital), 473 Hanzheng Street, Qiaokou District, Wuhan City, 430200 China.

Biomedical Engineering Letters

|July 8, 2025

Summary

This summary is machine-generated.

This study introduces an expert-informed active learning method for pulmonary nodule segmentation in CT scans. It enhances diagnostic accuracy by integrating radiologist expertise into deep learning models.

Keywords:

Active learning Expert Pulmonary nodule Segmentation

More Related Videos

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

$Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy$

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

Related Experiment Videos

Last Updated: Sep 16, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

$Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy$

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Accurate pulmonary nodule segmentation from CT images is crucial for lung cancer diagnosis and treatment.
Current segmentation algorithms often lack radiologist expertise, limiting model generalizability and trustworthiness.

Purpose of the Study:

To develop an expert-informed active learning method for pulmonary nodule segmentation.
To effectively integrate radiologist knowledge into deep segmentation models for improved accuracy and reliability.

Main Methods:

Developed an active learning scheme to iteratively optimize a deep segmentation model.
Combined uncertainties from segmentation results and radiologist corrections.
Utilized interactive graph interfaces for online expert knowledge integration.

Main Results:

The proposed method significantly improved pulmonary nodule segmentation performance on the Luna16 dataset.
Demonstrated effective incorporation of multiple radiologists' expertise into deep learning algorithms.

Conclusions:

The expert-informed approach enhances segmentation performance, validity, reliability, and generalizability of computer-aided diagnosis.
Facilitates integration of expert knowledge, leading to more trustworthy AI in medical imaging.