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Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging.

Fereshteh Yousefirizi1, Abhinav K Jha2, Julia Brosch-Lenz1

  • 1Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) image segmentation shows promise for medical imaging, especially PET scans. This review covers AI methods and evaluation criteria for clinical use, addressing data limitations with semi-supervised and unsupervised approaches.

Keywords:
Artificial intelligenceConvolutional neural networkMetabolically active tumor volumeNuclear medicinePETSegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • AI techniques, particularly convolutional neural networks, are advancing medical image segmentation.
  • Supervised AI methods require extensive annotated data, which is often limited and labor-intensive to obtain.
  • Semi-supervised and unsupervised AI methods offer solutions for segmentation with less annotated data.

Purpose of the Study:

  • To review current AI techniques for image-based segmentation in medical imaging.
  • To discuss evaluation criteria for the clinical translation of AI segmentation tools.
  • To highlight methods addressing data scarcity in AI-driven segmentation.

Main Methods:

  • Review of existing literature on AI for medical image segmentation.
  • Categorization of AI techniques (supervised, semi-supervised, unsupervised).
  • Analysis of evaluation metrics for translational AI research.

Main Results:

  • Convolutional neural networks demonstrate significant potential for automated segmentation, especially in PET imaging.
  • Semi-supervised and unsupervised AI methods are explored to overcome data limitations.
  • Various AI techniques are applicable to segmenting tumors and organs in different imaging modalities.

Conclusions:

  • AI-powered segmentation is a rapidly evolving field with high potential for clinical impact.
  • Addressing data annotation challenges is crucial for widespread AI adoption in medical imaging.
  • Standardized evaluation is necessary for the reliable translation of AI segmentation tools into clinical practice.