2022
Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography
Ahn S, Ta K, Thorn S, Onofrey J, Melvinsdottir I, Lee S, Langdon J, Sinusas A, Duncan J. Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography. Medical Image Analysis 2022, 84: 102711. PMID: 36525845, PMCID: PMC9812938, DOI: 10.1016/j.media.2022.102711.Peer-Reviewed Original ResearchConceptsSpatial transformer networkMotion trackingNoisy displacement fieldReliable motion estimationMotion tracking methodCardiac strain analysisTransformer networkDisplacement fieldDisplacement pathsMotion fieldTracking methodMotion estimationExperimental resultsStrain analysisSuperior performanceTemporal constraintsCardiac motionTrackingRegularization functionDependent featuresEchocardiography imagesNetworkPrior assumptionsField
2017
Flow Network Based Cardiac Motion Tracking Leveraging Learned Feature Matching
Parajuli N, Lu A, Stendahl J, Zontak M, Boutagy N, Alkhalil I, Eberle M, Lin B, O’Donnell M, Sinusas A, Duncan J. Flow Network Based Cardiac Motion Tracking Leveraging Learned Feature Matching. Lecture Notes In Computer Science 2017, 10434: 279-286. DOI: 10.1007/978-3-319-66185-8_32.Peer-Reviewed Original ResearchCardiac motion trackingNeural networkMotion trackingTedious feature engineeringSiamese neural networkMotion tracking methodFeature engineeringSiamese networkFeature matchingGraph nodesImage patchesSpatiotemporal problemsTracking algorithmTracking methodEdge weightsNetworkLinear programmingConsistent constraintsAdditional important contributionTrackingAlgorithmDatasetProgrammingNodesMatching