382 research outputs found
Fast Optimal Transport Averaging of Neuroimaging Data
Knowing how the Human brain is anatomically and functionally organized at the
level of a group of healthy individuals or patients is the primary goal of
neuroimaging research. Yet computing an average of brain imaging data defined
over a voxel grid or a triangulation remains a challenge. Data are large, the
geometry of the brain is complex and the between subjects variability leads to
spatially or temporally non-overlapping effects of interest. To address the
problem of variability, data are commonly smoothed before group linear
averaging. In this work we build on ideas originally introduced by Kantorovich
to propose a new algorithm that can average efficiently non-normalized data
defined over arbitrary discrete domains using transportation metrics. We show
how Kantorovich means can be linked to Wasserstein barycenters in order to take
advantage of an entropic smoothing approach. It leads to a smooth convex
optimization problem and an algorithm with strong convergence guarantees. We
illustrate the versatility of this tool and its empirical behavior on
functional neuroimaging data, functional MRI and magnetoencephalography (MEG)
source estimates, defined on voxel grids and triangulations of the folded
cortical surface.Comment: Information Processing in Medical Imaging (IPMI), Jun 2015, Isle of
Skye, United Kingdom. Springer, 201
Population-based fitting of medial shape models with correspondence optimization
pre-printA crucial problem in statistical shape analysis is establishing the correspondence of shape features across a population. While many solutions are easy to express using boundary representations, this has been a considerable challenge for medial representations. This paper uses a new 3-D medial model that allows continuous interpolation of the medial manifold and provides a map back and forth between it and the boundary. A measure defined on the medial surface then allows one to write integrals over the boundary and the object interior in medial coordinates, enabling the expression of important object properties in an object-relative coordinate system.We use these integrals to optimize correspondence during model construction, reducing variability due to the model parameterization that could potentially mask true shape change effects. Discrimination and hypothesis testing of populations of shapes are expected to benefit, potentially resulting in improved significance of shape differences between populations even with a smaller sample size
HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
The wide range of research in deep learning-based medical image segmentation
pushed the boundaries in a multitude of applications. A clinically relevant
problem that received less attention is the handling of scans with irregular
anatomy, e.g., after organ resection. State-of-the-art segmentation models
often lead to organ hallucinations, i.e., false-positive predictions of organs,
which cannot be alleviated by oversampling or post-processing. Motivated by the
increasing need to develop robust deep learning models, we propose HALOS for
abdominal organ segmentation in MR images that handles cases after organ
resection surgery. To this end, we combine missing organ classification and
multi-organ segmentation tasks into a multi-task model, yielding a
classification-assisted segmentation pipeline. The segmentation network learns
to incorporate knowledge about organ existence via feature fusion modules.
Extensive experiments on a small labeled test set and large-scale UK Biobank
data demonstrate the effectiveness of our approach in terms of higher
segmentation Dice scores and near-to-zero false positive prediction rate.Comment: To be published in proceedings of Information Processing In Medical
Imaging (IPMI) 202
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