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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications.

Cesar A Sierra-Franco1, Jan Hurtado2, Victor de A Thomaz1

  • 1Tecgraf Institute and Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.

Journal of Imaging Informatics in Medicine
|December 11, 2024
PubMed
Summary

This study introduces a deep learning framework for segmenting key anatomical structures in mammography images, creating the largest dataset for this task. The framework enables automated clinical applications for improved breast cancer detection and diagnosis.

Keywords:
ApplicationsDatasetDeep learningMammographySemantic segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Mammography is crucial for detecting non-palpable breast lesions, aiding early cancer detection.
  • Computer-aided detection (CAD) systems automate landmark segmentation for improved abnormality localization and image quality assessment.

Purpose of the Study:

  • To present a deep learning framework for segmenting nipple, pectoral muscle, fibroglandular, and fatty tissues in mammography.
  • To introduce the largest dataset for mammography segmentation to train deep learning models.
  • To demonstrate the framework's potential for clinical integration and diverse applications.

Main Methods:

  • Developed a deep learning-based framework for semantic segmentation of anatomical structures in mammography.
  • Utilized the largest dedicated dataset for mammography segmentation to train and evaluate models.
  • Compared four semantic segmentation architectures to assess suitability and flexibility.

Main Results:

  • Achieved robust segmentation performance across diverse and challenging mammography cases.
  • Demonstrated the suitability of multiple deep learning architectures for mammography segmentation.
  • Developed derived applications including lesion registration, density measurement, and quality assessment.

Conclusions:

  • The deep learning framework offers effective segmentation of key mammography structures.
  • The developed dataset and framework support advancements in computer-aided breast cancer detection.
  • Derived applications show potential for enhancing clinical workflows in breast cancer screening and diagnosis.