Background Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. Objective This article describes a semi-automatic monitoring approach using longitudinal medical images. We test the method on brain scans of meningioma patients, which experts found difficult to monitor as the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. Methods We describe a semi-automatic procedure targeted towards identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than five minutes, returns the total volume of tumor change in mm3. We test the method on post-gadolinium, T1-weighted Magnetic Resonance Images of ten meningioma patients and compare our results to experts’ findings. We also perform benchmark testing with synthetic data. Results Our experiments indicated that experts’ visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts’ manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts’ results. However, our approach required far less user input and generated more consistent measurements. Conclusion The sensitivity of experts’ visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts’ segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.

Change detection is a critical task in the diagnosis of many slowly evolving pathologies. This paper describes an approach that semi-automatically performs this task using longitudinal medical images. We are specifically interested in meningiomas, which experts often find difficult to monitor as the tumor evolution can be obscured by image artifacts. We test the method on synthetic data with known tumor growth as well as ten clinical data sets. We show that the results of our approach highly correlate with expert findings but seem to be less impacted by inter-and intra-rater variability.