3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects
This work addresses the challenge of detecting and characterizing extremely small objects in medical imaging for clinical research, which is incremental as it adapts existing methods to 3D data and specific training needs.
The authors tackled the problem of detecting and characterizing extremely small objects (ESOs) in 3D medical images, such as those in cerebral small vessel disease, by redesigning an RCNN model for 3D data and proposing training strategies, achieving a method that handles noisy labels and small object sizes effectively.
Extremely small objects (ESO) have become observable on clinical routine magnetic resonance imaging acquisitions, thanks to a reduction in acquisition time at higher resolution. Despite their small size (usually $<$10 voxels per object for an image of more than $10^6$ voxels), these markers reflect tissue damage and need to be accounted for to investigate the complete phenotype of complex pathological pathways. In addition to their very small size, variability in shape and appearance leads to high labelling variability across human raters, resulting in a very noisy gold standard. Such objects are notably present in the context of cerebral small vessel disease where enlarged perivascular spaces and lacunes, commonly observed in the ageing population, are thought to be associated with acceleration of cognitive decline and risk of dementia onset. In this work, we redesign the RCNN model to scale to 3D data, and to jointly detect and characterise these important markers of age-related neurovascular changes. We also propose training strategies enforcing the detection of extremely small objects, ensuring a tractable and stable training process.