Robust medical image segmentation by adapting neural networks for each test image

Neerav Karani, Ertunc Erdil, Krishna Chaitanya, Ender Konukoglu

Keywords: medical image segmentation, cross-scanner robustness, domain generalization

TL;DR: A method to make CNNs more robust to scanner and protocol changes, by adapting them for each test image.

Abstract: Performance of convolutional neural networks (CNNs) used for medical image analyses degrades markedly when training and test images differ in terms of their acquisition details, such as the scanner model or the protocol. We tackle this issue for the task of image segmentation by adapting a CNN ($C$) for each test image. Specifically, we design $C$ as a concatenation of a shallow normalization CNN ($N$), followed by a deep CNN ($S$) that segments the normalized image. At test time, we adapt $N$ for each test image, guided by an implicit prior on the predicted labels, which is modelled using an independently trained denoising autoencoder ($D$). The method is validated on multi-center MRI datasets of 3 anatomies. This article is a short version of the journal paper~\cite{karani2021test}.

Paper Type: both

Primary Subject Area: Segmentation

Secondary Subject Area: Transfer Learning and Domain Adaptation

Paper Status: based on accepted/submitted journal paper

Source Code Url: https://github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization

Data Set Url: https://www.humanconnectome.org/, http://fcon_1000.projects.nitrc.org/indi/abide/, https://wiki.cancerimagingarchive.net/display/Public/NCI-ISBI+2013+Challenge+-+Automated+Segmentation+of+Prostate+Structures, https://promise12.grand-challenge.org/

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