269 research outputs found
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
TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance
We propose a geometric deep-learning-based framework, TractGeoNet, for
performing regression using diffusion magnetic resonance imaging (dMRI)
tractography and associated pointwise tissue microstructure measurements. By
employing a point cloud representation, TractGeoNet can directly utilize
pointwise tissue microstructure and positional information from all points
within a fiber tract. To improve regression performance, we propose a novel
loss function, the Paired-Siamese Regression loss, which encourages the model
to focus on accurately predicting the relative differences between regression
label scores rather than just their absolute values. In addition, we propose a
Critical Region Localization algorithm to identify highly predictive anatomical
regions within the white matter fiber tracts for the regression task. We
evaluate the effectiveness of the proposed method by predicting individual
performance on two neuropsychological assessments of language using a dataset
of 20 association white matter fiber tracts from 806 subjects from the Human
Connectome Project. The results demonstrate superior prediction performance of
TractGeoNet compared to several popular regression models. Of the twenty tracts
studied, we find that the left arcuate fasciculus tract is the most highly
predictive of the two studied language performance assessments. The localized
critical regions are widespread and distributed across both hemispheres and all
cerebral lobes, including areas of the brain considered important for language
function such as superior and anterior temporal regions, pars opercularis, and
precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric
deep learning to enhance the study of the brain's white matter fiber tracts and
to relate their structure to human traits such as language performance.Comment: 28 pages, 7 figure
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