ArticleInternational journal of computer assisted radiology and surgery2024
Universal detection and segmentation of lymph nodes in multi-parametric MRI.
Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Deep learning for malignant lymph node segmentation and detection: a review.Frontiers in immunology · 2025Review
- Classification of Multi-Parametric Body MRI Series Using Deep Learning.IEEE journal of biomedical and health informatics · 2024Article
- Segmentation of mediastinal lymph nodes in CT with anatomical priors.International journal of computer assisted radiology and surgery · 2024Article
- Detection of abdominopelvic lymph nodes in multi-parametric MRI.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
purposeReliable measurement of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies of the body plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Previous approaches do not adequately exploit the complementary sequences in mpMRI to universally detect and segment lymph nodes, and they have shown fairly limited performance.
methodsWe propose a computer-aided detection and segmentation pipeline to leverage the T2 fat-suppressed (T2FS) and diffusion-weighted imaging (DWI) series from a mpMRI study. The T2FS and DWI series in 38 studies (38 patients) were co-registered and blended together using a selective data augmentation technique, such that traits of both series were visible in the same volume. A mask RCNN model was subsequently trained for universal detection and segmentation of 3D LNs.
resultsExperiments on 18 test mpMRI studies revealed that the proposed pipeline achieved a precision of [Formula: see text]%, sensitivity of [Formula: see text]% at 4 false positives (FP) per volume, and dice score of [Formula: see text]%. This represented an improvement of [Formula: see text]% in precision, [Formula: see text]% in sensitivity at 4 FP/volume, and [Formula: see text]% in dice score, respectively, over current approaches evaluated on the same dataset.
conclusionOur pipeline universally detected and segmented both metastatic and non-metastatic nodes in mpMRI studies. At test time, the input data used by the trained model could either be the T2FS series alone or a blend of co-registered T2FS and DWI series. Contrary to prior work, this eliminated the reliance on both the T2FS and DWI series in a mpMRI study.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.