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Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs.

Dimitrios Bounias1,2,3, Ashish Singh1,2, Spyridon Bakas1,2,4

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Summary
This summary is machine-generated.

This study introduces a faster, more consistent medical image segmentation method using interactive machine learning (IML). The IML approach significantly improves speed and accuracy compared to manual segmentation for various cancer types and spleen imaging.

Keywords:
artificial intelligenceartificial intelligence segmentationcomputer tomographyimage segmentationmagnetic resonance imaging

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Area of Science:

  • Medical imaging analysis
  • Machine learning in healthcare
  • Computational pathology

Background:

  • Accurate medical image segmentation is crucial for diagnosis and treatment planning.
  • Manual segmentation is time-consuming and prone to inter-observer variability.
  • Existing automated methods often require extensive training data or lack generalizability.

Purpose of the Study:

  • To develop and evaluate a fast, accurate, and consistent general-purpose image segmentation method using interactive machine learning (IML).
  • To compare the performance of the IML method against manual segmentation in terms of speed, spatial overlap, and consistency.
  • To validate the method across diverse clinical applications including brain, breast, and lung cancer, as well as spleen imaging.

Main Methods:

  • Developed an IML-based segmentation method utilizing brief user annotations and an adaptive geodesic distance transform.
  • Trained an ensemble of Support Vector Machines (SVMs) to create patient-specific models for whole-image segmentation.
  • Evaluated the method retrospectively on cohorts of brain (20), breast (50), and lung (50) cancer patients, and spleen scans (20), with ground truth annotations.

Main Results:

  • The IML method was 53.1% faster on average than manual annotation.
  • Significant improvements in spatial overlap (Dice coefficient) were observed for spleen, breast tumors, and lung nodules compared to manual segmentation.
  • The IML method demonstrated superior intra-rater and inter-rater consistency across all evaluated cohorts, particularly for spleen and brain tumor sub-regions.

Conclusions:

  • The proposed IML method offers a significant advancement in medical image segmentation, providing substantial gains in speed and consistency.
  • The method's accuracy and reliability are validated across multiple clinically relevant imaging tasks.
  • The open-source release of the implementation via CaPTk and as an MITK plugin facilitates broader adoption and further research.