TY - JOUR
T1 - Increasing Accuracy of Optimal Surfaces Using Min-marginal Energies
AU - Petersen, Jens
AU - Arias-Lorza, Andres M
AU - Selvan, Raghavendra
AU - Bos, Daniel
AU - van der Lugt, Aad
AU - Pedersen, Jesper H
AU - Nielsen, Mads
AU - de Bruijne, Marleen
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Optimal surface methods are a class of graph cut methods posing surface estimation as an n-ary ordered labeling problem. They are used in medical imaging to find interacting and layered surfaces optimally and in low order polynomial time. Representing continuous surfaces with discrete sets of labels, however, leads to discretization errors and, if graph representations are made dense, excessive memory usage. Limiting memory usage and computation time of graph cut methods are important and graphs that locally adapt to the problem has been proposed as a solution. Min-marginal energies computed using dynamic graph cuts offer a way to estimate solution uncertainty and these uncertainties have been used to decide where graphs should be adapted. Adaptive graphs, however, introduce extra parameters, complexity, and heuristics.We propose a way to use min-marginal energies to estimate continuous solution labels that does not introduce extra parameters and show empirically on synthetic and medical imaging datasets that it leads to improved accuracy. The increase in accuracy was consistent and in many cases comparable to accuracy otherwise obtained with graphs up to 8 times denser, but with proportionally less memory usage and improvements in computation time.
AB - Optimal surface methods are a class of graph cut methods posing surface estimation as an n-ary ordered labeling problem. They are used in medical imaging to find interacting and layered surfaces optimally and in low order polynomial time. Representing continuous surfaces with discrete sets of labels, however, leads to discretization errors and, if graph representations are made dense, excessive memory usage. Limiting memory usage and computation time of graph cut methods are important and graphs that locally adapt to the problem has been proposed as a solution. Min-marginal energies computed using dynamic graph cuts offer a way to estimate solution uncertainty and these uncertainties have been used to decide where graphs should be adapted. Adaptive graphs, however, introduce extra parameters, complexity, and heuristics.We propose a way to use min-marginal energies to estimate continuous solution labels that does not introduce extra parameters and show empirically on synthetic and medical imaging datasets that it leads to improved accuracy. The increase in accuracy was consistent and in many cases comparable to accuracy otherwise obtained with graphs up to 8 times denser, but with proportionally less memory usage and improvements in computation time.
U2 - 10.1109/TMI.2018.2890386
DO - 10.1109/TMI.2018.2890386
M3 - Journal article
C2 - 30605096
VL - 38
SP - 1559
EP - 1568
JO - I E E E Transactions on Medical Imaging
JF - I E E E Transactions on Medical Imaging
SN - 0278-0062
IS - 7
M1 - 8599009
ER -