32 research outputs found
NISF: Neural Implicit Segmentation Functions
Segmentation of anatomical shapes from medical images has taken an important
role in the automation of clinical measurements. While typical deep-learning
segmentation approaches are performed on discrete voxels, the underlying
objects being analysed exist in a real-valued continuous space. Approaches that
rely on convolutional neural networks (CNNs) are limited to grid-like inputs
and not easily applicable to sparse or partial measurements. We propose a novel
family of image segmentation models that tackle many of CNNs' shortcomings:
Neural Implicit Segmentation Functions (NISF). Our framework takes inspiration
from the field of neural implicit functions where a network learns a mapping
from a real-valued coordinate-space to a shape representation. NISFs have the
ability to segment anatomical shapes in high-dimensional continuous spaces.
Training is not limited to voxelized grids, and covers applications with sparse
and partial data. Interpolation between observations is learnt naturally in the
training procedure and requires no post-processing. Furthermore, NISFs allow
the leveraging of learnt shape priors to make predictions for regions outside
of the original image plane. We go on to show the framework achieves dice
scores of 0.87 0.045 on a (3D+t) short-axis cardiac segmentation task
using the UK Biobank dataset. We also provide a qualitative analysis on our
frameworks ability to perform segmentation and image interpolation on unseen
regions of an image volume at arbitrary resolutions
-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
We explore an ensembled -net for fast parallel MR imaging, including
parallel coil networks, which perform implicit coil weighting, and sensitivity
networks, involving explicit sensitivity maps. The networks in -net are
trained in a supervised way, including content and GAN losses, and with various
ways of data consistency, i.e., proximal mappings, gradient descent and
variable splitting. A semi-supervised finetuning scheme allows us to adapt to
the k-space data at test time, which, however, decreases the quantitative
metrics, although generating the visually most textured and sharp images. For
this challenge, we focused on robust and high SSIM scores, which we achieved by
ensembling all models to a -net.Comment: fastMRI challenge submission (team: holykspace
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
We present a deep network interpolation strategy for accelerated parallel MR
image reconstruction. In particular, we examine the network interpolation in
parameter space between a source model that is formulated in an unrolled scheme
with L1 and SSIM losses and its counterpart that is trained with an adversarial
loss. We show that by interpolating between the two different models of the
same network structure, the new interpolated network can model a trade-off
between perceptual quality and fidelity.Comment: Presented at 2020 ISMRM Conference & Exhibition (Abstract #4958