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 -