TriadNet: Sampling-free predictive intervals for lesional volume in 3D
  brain MR images
Benjamin Lambert Florence Forbes Senan Doyle Michel Dojat
The volume of a brain lesion (e.g. infarct or tumor) is a powerful indicator
of patient prognosis and can be used to guide the therapeutic strategy.
Lesional volume estimation is usually performed by segmentation with deep
convolutional neural networks (CNN), currently the state-of-the-art approach.
However, to date, few work has been done to equip volume segmentation tools
with adequate quantitative predictive intervals, which can hinder their
usefulness and acceptation in clinical practice. In this work, we propose
TriadNet, a segmentation approach relying on a multi-head CNN architecture,
which provides both the lesion volumes and the associated predictive intervals
simultaneously, in less than a second. We demonstrate its superiority over
other solutions on BraTS 2021, a large-scale MRI glioblastoma image database.