Artificial Intelligence-powered automatic volume calculation in medical images – available tools, performance and challenges for nuclear medicine

Abstract

Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry. Liver volumetry has shown progression in accuracy with a decrease in error. If a relative error below 10 % is acceptable, ML-based liver volumetry can be considered reliable for standardized imaging protocols if used in patients without major anomalies. Similarly, ML-supported automatic kidney volumetry has also shown consistency and reliability in volumetric calculations. In contrast, AI-supported thyroid volumetry has not been extensively developed, despite initial works in 3D ultrasound showing promising results in terms of accuracy and reproducibility. Despite the advancements presented in the reviewed literature, the lack of standardization limits the generalizability of ML methods across diverse scenarios. The domain gap, i. e., the difference in probability distribution of training and inference data, is of paramount importance before clinical deployment of AI, to maintain accuracy and reliability in patient care. The increasing availability of improved segmentation tools is expected to further incorporate AI methods into routine workflows where volumetry will play a more prominent role in radionuclide therapy planning and quantitative follow-up of disease evolution.

Guardians of precision: advancing radiation protection, safety, and quality systems in nuclear medicine

“…When paired with advanced imaging techniques, those tools offer a much more in-depth view of physiological and pathological processes. AI algorithms, especially those harnessing the power of DL, can analyze vast datasets and highlight subtle patterns or abnormalities [ 25 , 32 , 33 ].…”

“…In essence, the realm of image interpretation in nuclear medicine is undergoing a transformative phase. While the tools and techniques are evolving, the core objective remains unchanged: to provide the highest quality of patient care [ 25 , 32 , 33 ].…”