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Related Experiment Video

Updated: Sep 13, 2025

Analysis of Lymph Node Volume by Ultra-High-Frequency Ultrasound Imaging in the Braf/Pten Genetically Engineered Mouse Model of Melanoma

Analysis of Lymph Node Volume by Ultra-High-Frequency Ultrasound Imaging in the Braf/Pten Genetically Engineered Mouse Model of Melanoma

Published on: September 8, 2021

Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation.

Tejas Sudharshan Mathai¹, Sungwon Lee¹, Thomas C Shen¹

¹Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.

|July 30, 2025

Summary

This summary is machine-generated.

This study introduces an improved AI pipeline for detecting lymph nodes (LNs) in multiparametric MRI (mpMRI). The method enhances accuracy in identifying both benign and metastatic nodes, aiding cancer staging.

Keywords:

DWI Deep Learning Detection Lymph Node MRI Multi-Parametric Selective Augmentation T2

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Last Updated: Sep 13, 2025

Analysis of Lymph Node Volume by Ultra-High-Frequency Ultrasound Imaging in the Braf/Pten Genetically Engineered Mouse Model of Melanoma

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Published on: September 8, 2021

Non-invasive Optical Imaging of the Lymphatic Vasculature of a Mouse

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Published on: March 8, 2013

High-frequency Ultrasound Imaging of Mouse Cervical Lymph Nodes

High-frequency Ultrasound Imaging of Mouse Cervical Lymph Nodes

Published on: July 25, 2015

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate lymph node (LN) localization in multiparametric MRI (mpMRI) is crucial for lymphadenopathy assessment and cancer staging.
Current LN sizing methods are challenging due to diverse appearances in mpMRI, risking missed smaller metastatic nodes.

Purpose of the Study:

To develop a universal pipeline for detecting both benign and metastatic lymph nodes in mpMRI for improved measurement.
To enhance the robustness and accuracy of lymph node detection using deep learning.

Main Methods:

Utilized the VFNet neural network for lymph node identification in T2 fat suppressed and diffusion-weighted imaging (DWI) sequences.
Employed a selective data augmentation technique, Intra-Label LISA (ILL), to improve model robustness.

Main Results:

Achieved a sensitivity of approximately 83% with ILL, compared to 80% without ILL, at 4 false positives per volume (FP/vol).
Demonstrated a ~9% sensitivity improvement over existing LN detection methods on mpMRI at 4 FP/vol.

Conclusions:

The proposed pipeline offers a robust solution for universal lymph node detection in mpMRI.
The VFNet model combined with ILL augmentation significantly improves detection sensitivity, aiding clinical workflow and cancer assessment.