TriadNet: Sampling-free predictive intervals for lesional volume in 3D
brain MR images
- URL: http://arxiv.org/abs/2307.15638v1
- Date: Fri, 28 Jul 2023 15:56:04 GMT
- Title: TriadNet: Sampling-free predictive intervals for lesional volume in 3D
brain MR images
- Authors: Benjamin Lambert, Florence Forbes, Senan Doyle and Michel Dojat
- Abstract summary: 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.
We demonstrate its superiority over other solutions on BraTS 2021, a large-scale MRI glioblastoma image database.
- Score: 1.2234742322758418
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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