9,308 research outputs found
Temporal Interpolation via Motion Field Prediction
Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high
contrast 4D MR imaging during free breathing and provides in-vivo observations
for treatment planning and guidance. Navigator slices are vital for
retrospective stacking of 2D data slices in this method. However, they also
prolong the acquisition sessions. Temporal interpolation of navigator slices an
be used to reduce the number of navigator acquisitions without degrading
specificity in stacking. In this work, we propose a convolutional neural
network (CNN) based method for temporal interpolation via motion field
prediction. The proposed formulation incorporates the prior knowledge that a
motion field underlies changes in the image intensities over time. Previous
approaches that interpolate directly in the intensity space are prone to
produce blurry images or even remove structures in the images. Our method
avoids such problems and faithfully preserves the information in the image.
Further, an important advantage of our formulation is that it provides an
unsupervised estimation of bi-directional motion fields. We show that these
motion fields can be used to halve the number of registrations required during
4D reconstruction, thus substantially reducing the reconstruction time.Comment: Submitted to 1st Conference on Medical Imaging with Deep Learning
(MIDL 2018), Amsterdam, The Netherland
DDFlow: Learning Optical Flow with Unlabeled Data Distillation
We present DDFlow, a data distillation approach to learning optical flow
estimation from unlabeled data. The approach distills reliable predictions from
a teacher network, and uses these predictions as annotations to guide a student
network to learn optical flow. Unlike existing work relying on hand-crafted
energy terms to handle occlusion, our approach is data-driven, and learns
optical flow for occluded pixels. This enables us to train our model with a
much simpler loss function, and achieve a much higher accuracy. We conduct a
rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012
and 2015 benchmarks, and show that our approach significantly outperforms all
existing unsupervised learning methods, while running at real time.Comment: 8 pages, AAAI 1
- …