Prior-knowledge-informed deep learning for lacune detection and quantification using multi-site brain MRI
This work addresses the need for more practical and reliable automatic detection of lacunes in cerebral small vessel disease and dementia, though it appears incremental as it builds on prior algorithms by adding a burden score.
The paper tackled the problem of detecting and quantifying lacunes in brain MRI, which is challenging due to their small size and sparsity, by developing a novel framework that outputs both detection and a categorical burden score, resulting in a method that simplifies and accelerates imaging assessment while reducing sensitivity to noisy labels.
Lacunes of presumed vascular origin, also referred to as lacunar infarcts, are important to assess cerebral small vessel disease and cognitive diseases such as dementia. However, visual rating of lacunes from imaging data is challenging, time-consuming, and rater-dependent, owing to their small size, sparsity, and mimics. Whereas recent developments in automatic algorithms have shown to make the detection of lacunes faster while preserving sensitivity, they also showed a large number of false positives, which makes them impractical for use in clinical practice or large-scale studies. Here, we develop a novel framework that, in addition to lacune detection, outputs a categorical burden score. This score could provide a more practical estimate of lacune presence that simplifies and effectively accelerates the imaging assessment of lacunes. We hypothesize that the combination of detection and the categorical score makes the procedure less sensitive to noisy labels.