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Cross-site Validation of AI Segmentation and Harmonization in Breast MRI

  • 0Department of Biomedical Engineering, The City College of the City University of New York, 160 Convent Ave, New York, NY, 10031, USA.
Journal of Imaging Informatics in Medicine +

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September 25, 2024

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September 25, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A novel deep learning model matches radiologist performance in segmenting breast cancers on MRI scans across multiple sites. This AI tool, trained on extensive data, offers reproducible results for improved cancer detection and diagnosis.

Area Of Science

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Oncology

Background

  • Accurate segmentation of breast cancer in MRI is crucial for diagnosis and treatment planning.
  • Variability in segmentation performance across different clinical sites poses a challenge for AI model generalizability.

Purpose Of The Study

  • To validate the cross-site performance of an automated breast cancer segmentation model using a 3D U-Net architecture.
  • To compare the AI model's segmentation accuracy against that of experienced radiologists.

Main Methods

  • A 3D U-Net model was trained on a large dataset of dynamic contrast-enhanced axial MRIs from Site 1.
  • Performance was evaluated on test data from three clinical sites and common public data, with radiologist segmentations serving as ground truth.
  • A supervised harmonization technique with an affine input layer was employed for Site 3 data.

Main Results

  • The AI model achieved segmentation performance comparable to radiologists across Sites 1, 2, and common public data (median Dice scores ranging from 0.86 to 0.93).
  • For Site 3, fine-tuning the model resulted in performance on par with radiologists (Dice score 0.88 vs. 0.89).
  • The AI model numerically outperformed 11 out of 12 individual radiologists on common test data.

Conclusions

  • A deep learning model, enhanced with a novel supervised harmonization technique, demonstrates radiologist-level performance in cross-site MRI breast cancer segmentation.
  • The study highlights the potential of reproducible AI tools to aid in radiological assessments.
  • Publicly released code and weights aim to foster further advancements in AI for radiology.

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