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Universal detection and segmentation of lymph nodes in multi-parametric MRI

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Reliable 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.

Methods

We 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.

Results

Experiments on 18 test mpMRI studies revealed that the proposed pipeline achieved a precision of \(\sim 58\)%, sensitivity of \(\sim 78\)% at 4 false positives (FP) per volume, and dice score of \(\sim 81\)%. This represented an improvement of \(\ge 12\)% in precision, \(\ge 15\)% in sensitivity at 4 FP/volume, and \(\ge 14\)% in dice score, respectively, over current approaches evaluated on the same dataset.

Conclusion

Our 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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Funding

This work was supported by the Intramural Research Programs of the NIH National Library of Medicine and NIH Clinical Center (Project Number 1Z01 CL040004). We also thank Jaclyn Burge for the helpful comments and suggestions.

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Authors and Affiliations

  1. National Institutes of Health (NIH) Clinical Center, Bethesda, MD, USA

    Tejas Sudharshan Mathai, Sungwon Lee, Thomas C. Shen, Daniel Elton & Ronald M. Summers

  2. National Library of Medicine, NIH, Bethesda, MD, USA

    Zhiyong Lu

Authors
  1. Tejas Sudharshan Mathai
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  2. Sungwon Lee
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  3. Thomas C. Shen
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  4. Daniel Elton
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  6. Ronald M. Summers
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Corresponding author

Correspondence to Tejas Sudharshan Mathai.

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Conflict of interest

RMS receives royalties from iCAD, Philips, PingAn, ScanMed, and Translation Holdings. His lab received research support from PingAn. The authors have no additional conflicts of interest to declare.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this study, informed consent was not required.

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Mathai, T.S., Lee, S., Shen, T.C. et al. Universal detection and segmentation of lymph nodes in multi-parametric MRI. Int J CARS 19, 163–170 (2024). https://doi.org/10.1007/s11548-023-02954-7

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  • DOI: https://doi.org/10.1007/s11548-023-02954-7

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