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.

翻译：脑部病灶（如梗死或肿瘤）体积是患者预后的重要指标，可用于指导治疗策略。病灶体积估计通常通过深度卷积神经网络（CNN）进行分割实现，这是目前最先进的方法。然而，迄今为止，很少有研究为体积分割工具配备适当的定量预测区间，这可能限制其在临床实践中的实用性和接受度。本文提出TriadNet，一种基于多头CNN架构的分割方法，可在不到一秒内同时提供病灶体积及相应的预测区间。我们在BraTS 2021（大规模MRI胶质母细胞瘤图像数据库）上证明了该方法相较于其他方案的优越性。