2020
A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking and Segmentation in 4D Echocardiography
Ta K, Ahn SS, Stendahl JC, Sinusas AJ, Duncan JS. A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking and Segmentation in 4D Echocardiography. Lecture Notes In Computer Science 2020, 12266: 468-477. PMID: 33094292, PMCID: PMC7576886, DOI: 10.1007/978-3-030-59725-2_45.Peer-Reviewed Original ResearchA Semi-Supervised Joint Learning Approach to Left Ventricular Segmentation and Motion Tracking in Echocardiography
Ta K, Ahn SS, Lu A, Stendahl JC, Sinusas AJ, Duncan JS. A Semi-Supervised Joint Learning Approach to Left Ventricular Segmentation and Motion Tracking in Echocardiography. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2020, 00: 1734-1737. PMID: 33005289, PMCID: PMC7526517, DOI: 10.1109/isbi45749.2020.9098664.Peer-Reviewed Original ResearchMotion trackingUnsupervised motion tracking of left ventricle in echocardiography
Ahn SS, Ta K, Lu A, Stendahl JC, Sinusas AJ, Duncan JS. Unsupervised motion tracking of left ventricle in echocardiography. Proceedings Of SPIE--the International Society For Optical Engineering 2020, 11319: 113190z-113190z-7. PMID: 32994659, PMCID: PMC7521020, DOI: 10.1117/12.2549572.Peer-Reviewed Original ResearchMotion trackingGround truth displacement fieldsConvolutional neural networkAccurate motion trackingDense displacement fieldB-mode echocardiography imagesU-NetNeural networkTracking frameworkNon-rigid registration algorithmTarget imageRegistration algorithmTarget frameSource frameAlgorithmEchocardiography imagesFavorable performanceDatasetImagesTrackingDisplacement estimationLarge amountEchocardiographic imagesSegmentationNetwork
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 contributionTrackingAlgorithmDatasetProgrammingNodesMatchingLearning-Based Spatiotemporal Regularization and Integration of Tracking Methods for Regional 4D Cardiac Deformation Analysis
Lu A, Zontak M, Parajuli N, Stendahl J, Boutagy N, Eberle M, Alkhalil I, O’Donnell M, Sinusas A, Duncan J. Learning-Based Spatiotemporal Regularization and Integration of Tracking Methods for Regional 4D Cardiac Deformation Analysis. Lecture Notes In Computer Science 2017, 10434: 323-331. DOI: 10.1007/978-3-319-66185-8_37.Peer-Reviewed Original ResearchDeformation analysisTracking methodCardiac motion trackingDeformation-based methodUltrasound imaging propertiesSurface trackingGood overall estimationCardiac deformation analysisMotion trackingSpatiotemporal regularizationInput sourcesDifferent input sourcesTracking resultsTrackingPerceptron networkRegularization procedureMulti-layered perceptron networkMethod