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Related Experiment Video

Updated: Jun 6, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

A fully automatic algorithm for segmentation of the breasts in DCE-MR images.

Valentina Giannini1, Anna Vignati, Lia Morra

  • 1Politecnico of Turin, Electronics Department, Italy. valentina.giannini@polito.it

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
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This study introduces a robust, automated breast and axillary region segmentation method for MRI. The novel approach, detecting pectoral muscle borders, improves lesion detection accuracy in dynamic contrast-enhanced-MR imaging.

Area of Science:

  • Medical Imaging
  • Radiology
  • Image Processing

Background:

  • Accurate breast and axillary region segmentation is crucial for automated lesion detection in breast MRI.
  • Existing segmentation methods can be sensitive to noise and field inhomogeneities.

Purpose of the Study:

  • To develop a fully automatic and robust segmentation procedure for the breast and axillary region in MRI.
  • To improve preprocessing for dynamic contrast-enhanced-MR (DCE-MR) studies.

Main Methods:

  • A novel automatic segmentation technique based on detecting the upper border of the pectoral muscle.
  • Comparison of the automated method against manual segmentation for quantitative evaluation.

Main Results:

  • The proposed method demonstrates robustness against noise and field inhomogeneities, outperforming thresholding-based techniques.

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

  • Quantitative evaluation on 31 cases showed good agreement with manual segmentation (overlap=0.79 ± 0.09, recall=0.95 ± 0.02, precision=0.82 ± 0.1).

    • Conclusions:

    • The developed automatic segmentation method is effective and reliable for breast and axillary regions in MRI.
    • This technique offers a significant advancement in preprocessing for automated lesion detection in DCE-MR imaging.