8 research outputs found
Brain vasculature segmentation from magnetic resonance angiographic image
Master'sMASTER OF ENGINEERIN
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
In this paper, we introduce a dictionary learning based approach applied to
the problem of real-time reconstruction of MR image sequences that are highly
undersampled in k-space. Unlike traditional dictionary learning, our method
integrates both global and patch-wise (local) sparsity information and
incorporates some priori information into the reconstruction process. Moreover,
we use a Dependent Hierarchical Beta-process as the prior for the group-based
dictionary learning, which adaptively infers the dictionary size and the
sparsity of each patch; and also ensures that similar patches are manifested in
terms of similar dictionary atoms. An efficient numerical algorithm based on
the alternating direction method of multipliers (ADMM) is also presented.
Through extensive experimental results we show that our proposed method
achieves superior reconstruction quality, compared to the other state-of-the-
art DL-based methods
End-to-end detection-segmentation network with ROI convolution
We propose an end-to-end neural network that improves the segmentation
accuracy of fully convolutional networks by incorporating a localization unit.
This network performs object localization first, which is then used as a cue to
guide the training of the segmentation network. We test the proposed method on
a segmentation task of small objects on a clinical dataset of ultrasound
images. We show that by jointly learning for detection and segmentation, the
proposed network is able to improve the segmentation accuracy compared to only
learning for segmentation. Code is publicly available at
https://github.com/vincentzhang/roi-fcn.Comment: ISBI 201
Vasculature segmentation in MRA images using gradient compensated geodesic active contours
10.1007/s11265-008-0216-4Journal of Signal Processing Systems541-3171-18