# 3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects

arXiv: https://arxiv.org/abs/1812.09046 · HTML: https://mlsanity.com/paper/1812.09046/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review](https://mlsanity.com/paper/2204.01411/) (2022)
- [Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI](https://mlsanity.com/paper/2209.13727/) (2022)
- [A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces](https://mlsanity.com/paper/2603.04243/) (2026)
- [Perivascular space Identification Nnunet for Generalised Usage (PINGU)](https://mlsanity.com/paper/2405.08337/) (2024)
- [Automated neuroradiological support systems for multiple cerebrovascular disease markers -- A systematic review and meta-analysis](https://mlsanity.com/paper/2410.17124/) (2024)

## Abstract (arXiv)

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 106 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.