Data-driven feature learning for myocardial segmentation of CP-BOLD MRI

TY - JOUR

T1 - Data-driven feature learning for myocardial segmentation of CP-BOLD MRI

AU - Mukhopadhyay, Anirban

AU - Oksuz, Ilkay

AU - Bevilacqua, Marco

AU - Dharmakumar, Rohan

AU - Tsaftaris, Sotirios A.

PY - 2015

Y1 - 2015

N2 - Multi–slice computed tomography image series are a valuable source of information to extract shape and motion parameters of the heart. We present a method how to segment and label all main chambers (both ventricles and atria) and connected vessels (arteries and main vein trunks) from such images and to track their movement over the cardiac cycle. A framework is presented to construct a multi–surface triangular model enclosing all blood–filled cavities and the main myocardium as well as to adapt this model to unseen images, and to propagate it from phase to phase. While model construction still requires a reasonable amount of user interaction, adaptation is mostly automated, and propagation works fully automatically. The adaptation method by deformable surface models requires a set of landmarks to be manually located for one of the cardiac phases for model initialisation.

AB - Multi–slice computed tomography image series are a valuable source of information to extract shape and motion parameters of the heart. We present a method how to segment and label all main chambers (both ventricles and atria) and connected vessels (arteries and main vein trunks) from such images and to track their movement over the cardiac cycle. A framework is presented to construct a multi–surface triangular model enclosing all blood–filled cavities and the main myocardium as well as to adapt this model to unseen images, and to propagate it from phase to phase. While model construction still requires a reasonable amount of user interaction, adaptation is mostly automated, and propagation works fully automatically. The adaptation method by deformable surface models requires a set of landmarks to be manually located for one of the cardiac phases for model initialisation.

KW - CINE MR

KW - CP-BOLD MR

KW - Dictionary learning

KW - Segmentation

U2 - 10.1007/978-3-319-20309-6_22

DO - 10.1007/978-3-319-20309-6_22

M3 - Conference paper

C2 - 4520227

SP - 189

EP - 197

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -