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Automatization of CT Annotation: Combining AI Efficiency with Expert Precision.

Edgars Edelmers¹, Dzintra Kazoka¹, Katrina Bolocko²

¹Institute of Anatomy and Anthropology, Rīga Stradiņš University, LV-1010 Riga, Latvia.

Diagnostics (Basel, Switzerland)

|January 22, 2024

Summary

This study introduces a new AI-driven method for segmenting vertebral columns in CT scans. It uses semi-automated labeling and manual validation to improve speed and accuracy in medical imaging diagnostics.

Keywords:

annotation artificial intelligence computer vision radiology semantic segmentation

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Medical imaging diagnostics are being transformed by artificial intelligence (AI), machine learning (ML), and deep learning (DL).
Accurate segmentation of anatomical structures in CT scans is crucial for diagnosis and research.

Purpose of the Study:

To present a novel methodology for semantic segmentation of the vertebral column in CT scans.
To develop a semi-automated annotation system for efficient and accurate labeling of vertebrae.

Main Methods:

Utilized a dataset of 250 patients from Riga East Clinical University Hospital.
Developed a semi-automated annotation system for initial data labeling by non-experts, followed by manual validation.
Streamlined the segmentation process by eliminating the need for volume stitching.

Main Results:

Achieved accurate identification and labeling of individual vertebrae from C1 to the sacrum-coccyx complex.
Significantly reduced the time and resources required for vertebral column segmentation.
Demonstrated enhanced external validity through meticulous patient selection and demographic balancing.

Conclusions:

The proposed AI-driven methodology represents a substantial advancement in medical data semantic segmentation.
This approach has the potential to revolutionize clinical and research practices in radiology by improving efficiency and accuracy.
Challenges include the need for manual validation by skilled personnel and reliance on specialized hardware.