34 research outputs found
Scribble-based Domain Adaptation via Co-segmentation
Although deep convolutional networks have reached state-of-the-art
performance in many medical image segmentation tasks, they have typically
demonstrated poor generalisation capability. To be able to generalise from one
domain (e.g. one imaging modality) to another, domain adaptation has to be
performed. While supervised methods may lead to good performance, they require
to fully annotate additional data which may not be an option in practice. In
contrast, unsupervised methods don't need additional annotations but are
usually unstable and hard to train. In this work, we propose a novel
weakly-supervised method. Instead of requiring detailed but time-consuming
annotations, scribbles on the target domain are used to perform domain
adaptation. This paper introduces a new formulation of domain adaptation based
on structured learning and co-segmentation. Our method is easy to train, thanks
to the introduction of a regularised loss. The framework is validated on
Vestibular Schwannoma segmentation (T1 to T2 scans). Our proposed method
outperforms unsupervised approaches and achieves comparable performance to a
fully-supervised approach.Comment: Accepted at MICCAI 202
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide
variety of approaches have been proposed. However, only a few studies have
comprehensively compared medical image registration approaches on a wide range
of clinically relevant tasks. This limits the development of registration
methods, the adoption of research advances into practice, and a fair benchmark
across competing approaches. The Learn2Reg challenge addresses these
limitations by providing a multi-task medical image registration data set for
comprehensive characterisation of deformable registration algorithms. A
continuous evaluation will be possible at
https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of
anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR),
availability of annotations, as well as intra- and inter-patient registration
evaluation. We established an easily accessible framework for training and
validation of 3D registration methods, which enabled the compilation of results
of over 65 individual method submissions from more than 20 unique teams. We
used a complementary set of metrics, including robustness, accuracy,
plausibility, and runtime, enabling unique insight into the current
state-of-the-art of medical image registration. This paper describes datasets,
tasks, evaluation methods and results of the challenge, as well as results of
further analysis of transferability to new datasets, the importance of label
supervision, and resulting bias. While no single approach worked best across
all tasks, many methodological aspects could be identified that push the
performance of medical image registration to new state-of-the-art performance.
Furthermore, we demystified the common belief that conventional registration
methods have to be much slower than deep-learning-based methods