Edgars Edelmers, Dzintra Kazoka, Katrina Bolocko, Kaspars Sudars, Mara Pilmane. Automatization of CT Annotation: Combining AI Efficiency with Expert Precision. Diagnostics, 14(2), 185 pp. MDPI, 2024.
Bibtex citāts:
Bibtex citāts:
@article{15963_2024,
author = {Edgars Edelmers and Dzintra Kazoka and Katrina Bolocko and Kaspars Sudars and Mara Pilmane},
title = {Automatization of CT Annotation: Combining AI Efficiency with Expert Precision},
journal = {Diagnostics},
volume = {14},
issue = {2},
pages = {185},
publisher = {MDPI},
year = {2024}
}
author = {Edgars Edelmers and Dzintra Kazoka and Katrina Bolocko and Kaspars Sudars and Mara Pilmane},
title = {Automatization of CT Annotation: Combining AI Efficiency with Expert Precision},
journal = {Diagnostics},
volume = {14},
issue = {2},
pages = {185},
publisher = {MDPI},
year = {2024}
}
Anotācija: The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum–coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology.
Žurnāla kvartile: Q2