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Related Experiment Video

Updated: Mar 1, 2026

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
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MONITORING SLOWLY EVOLVING TUMORS.

E Konukoglu1, W M Wells2, S Novellas1

  • 1Asclepios Research Project, INRIA, Sophia Antipolis, France.

Proceedings. IEEE International Symposium on Biomedical Imaging
|June 9, 2017
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Summary
This summary is machine-generated.

This study introduces a semi-automatic method for change detection in longitudinal medical images, aiding in monitoring slow-progressing pathologies like meningiomas. The approach correlates well with expert assessments and reduces variability in tumor monitoring.

Keywords:
follow-uptime series analysistumor

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Area of Science:

  • Medical Imaging Analysis
  • Radiology
  • Computational Pathology

Background:

  • Change detection is crucial for diagnosing slowly evolving diseases.
  • Monitoring pathologies like meningiomas is challenging due to image artifacts and expert variability.
  • Longitudinal medical image analysis requires robust automated or semi-automated tools.

Purpose of the Study:

  • To develop and validate a semi-automatic method for change detection in longitudinal medical images.
  • To assess the method's efficacy in monitoring meningioma evolution.
  • To reduce inter- and intra-rater variability in the assessment of tumor changes.

Main Methods:

  • A semi-automatic approach for change detection using longitudinal medical imaging data.
  • Testing on synthetic data with controlled tumor growth.
  • Validation on ten clinical datasets of meningioma patients.

Main Results:

  • The developed method shows high correlation with expert findings in change detection.
  • The approach is less affected by inter- and intra-rater variability compared to manual expert assessment.
  • Successful application demonstrated on both synthetic and real clinical data.

Conclusions:

  • The semi-automatic change detection method provides a reliable tool for monitoring slow pathologies like meningiomas.
  • This approach can enhance diagnostic accuracy and consistency in clinical practice.
  • The method offers a promising solution to overcome limitations in expert-based image analysis.