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Related Experiment Video

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Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks.

Oscar A Debats1, Geert J S Litjens1, Henkjan J Huisman1

  • 1Department of Radiology and Nuclear Medicine, Radboudumc, Nijmegen, The Netherlands.

Peerj
|November 28, 2019
PubMed
Summary

Multi-view convolutional neural networks significantly reduced false positives in lymph node detection for prostate cancer MR Lymphography. Three orthogonal views proved sufficient, enhancing computer-aided detection system performance.

Keywords:
Machine learningMagnetic resonance lymphographyMulti-view convolutional neural networksProstate cancer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Oncology

Background:

  • Prostate cancer diagnosis relies on accurate lymph node assessment.
  • MR Lymphography (MRL) is used for imaging pelvic lymph nodes.
  • Automated detection systems aim to improve efficiency and accuracy.

Purpose of the Study:

  • To evaluate if multi-view convolutional neural networks (CNNs) enhance automated lymph node detection in MRL images for prostate cancer patients.
  • To assess the effectiveness of different multi-view CNN configurations in reducing false positives.

Main Methods:

  • A previously developed computer-aided detection (CAD) system was enhanced with 2D multi-view CNNs (1-view, 3-view, 9-view).
  • Deep learning models were trained on MRL data from 240 prostate cancer patients (ferumoxtran-10 contrast).
  • Performance was compared with and without CNNs using FROC analysis and partial area under the FROC curve.

Main Results:

  • Multi-view CNNs significantly reduced false positive lymph node detections (p < 0.01).
  • The 3-view and 9-view CNNs outperformed the 1-view CNN.
  • False positives decreased from 20.6 to 7.8 per image at 80% sensitivity.

Conclusions:

  • Multi-view CNNs significantly improve lymph node detection in MRL by reducing false positives.
  • Three orthogonal views are sufficient for effective CAD system performance.
  • This CAD system can potentially expedite lymph node identification and metastasis assessment in MRL.