6,992 research outputs found
Fast Predictive Multimodal Image Registration
We introduce a deep encoder-decoder architecture for image deformation
prediction from multimodal images. Specifically, we design an image-patch-based
deep network that jointly (i) learns an image similarity measure and (ii) the
relationship between image patches and deformation parameters. While our method
can be applied to general image registration formulations, we focus on the
Large Deformation Diffeomorphic Metric Mapping (LDDMM) registration model. By
predicting the initial momentum of the shooting formulation of LDDMM, we
preserve its mathematical properties and drastically reduce the computation
time, compared to optimization-based approaches. Furthermore, we create a
Bayesian probabilistic version of the network that allows evaluation of
registration uncertainty via sampling of the network at test time. We evaluate
our method on a 3D brain MRI dataset using both T1- and T2-weighted images. Our
experiments show that our method generates accurate predictions and that
learning the similarity measure leads to more consistent registrations than
relying on generic multimodal image similarity measures, such as mutual
information. Our approach is an order of magnitude faster than
optimization-based LDDMM.Comment: Accepted as a conference paper for ISBI 201
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
Multi-task neural network architectures provide a mechanism that jointly
integrates information from distinct sources. It is ideal in the context of
MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT)
scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic
multi-task network that estimates: 1) intrinsic uncertainty through a
heteroscedastic noise model for spatially-adaptive task loss weighting and 2)
parameter uncertainty through approximate Bayesian inference. This allows
sampling of multiple segmentations and synCTs that share their network
representation. We test our model on prostate cancer scans and show that it
produces more accurate and consistent synCTs with a better estimation in the
variance of the errors, state of the art results in OAR segmentation and a
methodology for quality assurance in radiotherapy treatment planning.Comment: Early-accept at MICCAI 2018, 8 pages, 4 figure
Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission
tomography (PET) image reconstruction to allow for accurate quantification.
However, attenuation correction is particularly challenging for PET-MRI as
neither PET nor magnetic resonance imaging (MRI) can directly image tissue
attenuation properties. MRI-based computed tomography (CT) synthesis has been
proposed as an alternative to physics based and segmentation-based approaches
that assign a population-based tissue density value in order to generate an
attenuation map. We propose a novel deep fully convolutional neural network
that generates synthetic CTs in a recursive manner by gradually reducing the
residuals of the previous network, increasing the overall accuracy and
generalisability, while keeping the number of trainable parameters within
reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT
pairs and a four-fold random bootstrapped validation with a 80:20 split is
performed. Quantitative results show that the proposed framework outperforms a
state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE)
from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction
error from 14.3% to 7.2%.Comment: Accepted at SASHIMI201
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