617 research outputs found
Recursive Cascaded Networks for Unsupervised Medical Image Registration
We present recursive cascaded networks, a general architecture that enables
learning deep cascades, for deformable image registration. The proposed
architecture is simple in design and can be built on any base network. The
moving image is warped successively by each cascade and finally aligned to the
fixed image; this procedure is recursive in a way that every cascade learns to
perform a progressive deformation for the current warped image. The entire
system is end-to-end and jointly trained in an unsupervised manner. In
addition, enabled by the recursive architecture, one cascade can be iteratively
applied for multiple times during testing, which approaches a better fit
between each of the image pairs. We evaluate our method on 3D medical images,
where deformable registration is most commonly applied. We demonstrate that
recursive cascaded networks achieve consistent, significant gains and
outperform state-of-the-art methods. The performance reveals an increasing
trend as long as more cascades are trained, while the limit is not observed.
Code is available at https://github.com/microsoft/Recursive-Cascaded-Networks.Comment: Accepted to ICCV 201
CNN-based Lung CT Registration with Multiple Anatomical Constraints
Deep-learning-based registration methods emerged as a fast alternative to
conventional registration methods. However, these methods often still cannot
achieve the same performance as conventional registration methods because they
are either limited to small deformation or they fail to handle a superposition
of large and small deformations without producing implausible deformation
fields with foldings inside.
In this paper, we identify important strategies of conventional registration
methods for lung registration and successfully developed the deep-learning
counterpart. We employ a Gaussian-pyramid-based multilevel framework that can
solve the image registration optimization in a coarse-to-fine fashion.
Furthermore, we prevent foldings of the deformation field and restrict the
determinant of the Jacobian to physiologically meaningful values by combining a
volume change penalty with a curvature regularizer in the loss function.
Keypoint correspondences are integrated to focus on the alignment of smaller
structures.
We perform an extensive evaluation to assess the accuracy, the robustness,
the plausibility of the estimated deformation fields, and the transferability
of our registration approach. We show that it achieves state-of-the-art results
on the COPDGene dataset compared to conventional registration method with much
shorter execution time. In our experiments on the DIRLab exhale to inhale lung
registration, we demonstrate substantial improvements (TRE below mm) over
other deep learning methods. Our algorithm is publicly available at
https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
In cardiac magnetic resonance imaging, fully-automatic segmentation of the
heart enables precise structural and functional measurements to be taken, e.g.
from short-axis MR images of the left-ventricle. In this work we propose a
recurrent fully-convolutional network (RFCN) that learns image representations
from the full stack of 2D slices and has the ability to leverage inter-slice
spatial dependences through internal memory units. RFCN combines anatomical
detection and segmentation into a single architecture that is trained
end-to-end thus significantly reducing computational time, simplifying the
segmentation pipeline, and potentially enabling real-time applications. We
report on an investigation of RFCN using two datasets, including the publicly
available MICCAI 2009 Challenge dataset. Comparisons have been carried out
between fully convolutional networks and deep restricted Boltzmann machines,
including a recurrent version that leverages inter-slice spatial correlation.
Our studies suggest that RFCN produces state-of-the-art results and can
substantially improve the delineation of contours near the apex of the heart.Comment: MICCAI Workshop RAMBO 201
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