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Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm.

J Jayender1, K G Vosburgh1, E Gombos1

  • 1Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|June 13, 2017
PubMed
Summary
This summary is machine-generated.

A new Statistical Learning Algorithm for Tumor Segmentation (SLATS) automates cancer detection in dynamic contrast-enhanced MRI (DCE-MRI). This tool shows high accuracy and sensitivity, aiding in tumor delineation for image-guided interventions.

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Area of Science:

  • Medical Imaging
  • Machine Learning in Oncology
  • Radiology

Background:

  • Segmenting malignant tumors from DCE-MRI is complex, time-consuming, and requires 4D data processing.
  • Existing quantitative analyses are sensitive to external factors and may not apply to breast carcinomas.

Purpose of the Study:

  • To develop a novel algorithm for automatic tumor segmentation from DCE-MRI data.
  • To evaluate the accuracy and sensitivity of the developed algorithm in comparison to expert radiologists.

Main Methods:

  • Development of a Statistical Learning Algorithm for Tumor Segmentation (SLATS).
  • User-guided region selection on DCE-MRI for automated segmentation.
  • Comparison of SLATS results with expert radiologist segmentations.

Main Results:

  • SLATS demonstrated 78% accuracy in segmenting cancers from DCE-MRI.
  • SLATS achieved 100% sensitivity in cancer segmentation.
  • The algorithm's performance was validated against expert radiologist segmentations.

Conclusions:

  • SLATS is a promising tool for automated tumor segmentation in DCE-MRI.
  • The algorithm shows high accuracy and sensitivity, potentially assisting in image-guided interventions.
  • Further studies are warranted to confirm its utility in clinical practice.