7 research outputs found
Unbiased Shape Compactness for Segmentation
We propose to constrain segmentation functionals with a dimensionless,
unbiased and position-independent shape compactness prior, which we solve
efficiently with an alternating direction method of multipliers (ADMM).
Involving a squared sum of pairwise potentials, our prior results in a
challenging high-order optimization problem, which involves dense (fully
connected) graphs. We split the problem into a sequence of easier sub-problems,
each performed efficiently at each iteration: (i) a sparse-matrix inversion
based on Woodbury identity, (ii) a closed-form solution of a cubic equation and
(iii) a graph-cut update of a sub-modular pairwise sub-problem with a sparse
graph. We deploy our prior in an energy minimization, in conjunction with a
supervised classifier term based on CNNs and standard regularization
constraints. We demonstrate the usefulness of our energy in several medical
applications. In particular, we report comprehensive evaluations of our fully
automated algorithm over 40 subjects, showing a competitive performance for the
challenging task of abdominal aorta segmentation in MRI.Comment: Accepted at MICCAI 201
Segmentation of PolSAR image by using an automatic initialized variational model and a dual optimization approach
Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets
This paper proposed an automated 3D lumbar intervertebral disc (IVD)
segmentation strategy from MRI data. Starting from two user supplied landmarks,
the geometrical parameters of all lumbar vertebral bodies and intervertebral discs
are automatically extracted from a mid-sagittal slice using a graphical model based
approach. After that, a three-dimensional (3D) variable-radius soft tube model of the
lumbar spine column is built to guide the 3D disc segmentation. The disc segmentation
is achieved as a multi-kernel diffeomorphic registration between a 3D template
of the disc and the observed MRI data. Experiments on 15 patient data sets showed
the robustness and the accuracy of the proposed algorithm