2024
A sequential geometry-reconstruction-based deep learning approach to improve accuracy and consistence of lumbar spine MRI image segmentation
Qian L, Chen J, Ma L, Urakov T, Liang L. A sequential geometry-reconstruction-based deep learning approach to improve accuracy and consistence of lumbar spine MRI image segmentation. Progress In Biomedical Optics And Imaging 2024, 12926: 1292634-1292634-8. DOI: 10.1117/12.3007064.Peer-Reviewed Original ResearchDeep learning approachImage segmentationLearning approachMedical image segmentationSelf-attention mechanismAccurate semantic segmentationImage feature extractionMRI image segmentationPosition embeddingsSemantic segmentationSwin-UnetFeature extractionErroneous fragmentsSpine MRI imagesShape representationNeural networkSegmentation resultsTexture similaritySegmentation modelAttention UNetMRI imagesGeometry reconstructionInternal informationIrrelevant informationImage features
2020
CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images
Qian L, Chen J, Urakov T, Gu W, Liang L. CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images. 2020, 00: 580-585. DOI: 10.1109/icmla51294.2020.00097.Peer-Reviewed Original ResearchContinuous latent spaceLatent spaceVariational autoencoderImage datasetsHuman expertsTraining samplesSpine MRI imagesProbabilistic outputsMatching algorithmMedical imagesProbability distributionRepresentation of ambiguityGenerative modelOutput distributionProbabilistic componentDiscrete probability distributionsDeterministic pathImage interpretationUncertainty estimationShape analysisMRI imagesMedical applicationsAutoencoderImagesBackpropagation