TriadNet: Sampling-free predictive intervals for lesional volume in 3D brain MR images

TL;DR

TriadNet is a fast, multi-head CNN that estimates brain lesion volumes along with predictive intervals, enhancing clinical decision-making without additional sampling.

Contribution

It introduces TriadNet, a novel sampling-free method for simultaneous lesion volume estimation and predictive interval generation in 3D brain MRI.

Findings

Outperforms existing methods on BraTS 2021 dataset

Provides rapid volume and uncertainty estimates in less than a second

Enhances clinical applicability of lesion volume analysis

Abstract

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.