Unlearning scanner bias for MRI harmonisation in medical image segmentation

Conference item : Presentation

The combination of datasets is vital for providing increased statistical power, and is especially important for neurological conditions where limited data is available. However, our ability to combine datasets is limited by the addition of variance caused by factors such as differences in acquisition protocol and hardware. We aim to create scanner-invariant features using an iterative training scheme based on domain adaptation techniques, whilst simultaneously completing the desired segmentation task. We demonstrate the technique using an encoder-decoder architecture similar to the U-Net but expect that the proposed training scheme would be applicable to any feedforward network and task. We show that the network can be used to harmonise two datasets and also show that the network is applicable in the common scenario of limited available training data, meaning that the network should be applicable for real-world segmentation problems.

Authors: Dinsdale, NK; Jenkinson, M; Namburete, AIL

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Host title: Communications in Computer and Information Science
• Series: Communications in Computer and Information Science
• Series number: 1248
• Publication date: 2020-07-15
• Acceptance date: 2020-05-04
• Event title: MIUA 2020: Medical Image Understanding and Analysis
• Event series: Annual Conference on Medical Image Understanding and Analysis
• Event location: Online
• Event website: https://miua2020.com/
• Event start date: 2020-07-15
• Event end date: 2020-10-17
• DOI: 10.1007/978-3-030-52791-4_2
• EISBN: 978-3-030-52791-4
• ISBN: 978-3-030-52790-7
• Language: English
• Keywords: harmonisation; MRI; joint domain adaptation; FFR
• Subtype: Presentation