A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence
Tran A, Desser D, Zeevi T, Abou Karam G, Dierksen F, Dell’Orco A, Kniep H, Hanning U, Fiehler J, Zietz J, Sanelli P, Malhotra A, Duncan J, Aneja S, Falcone G, Qureshi A, Sheth K, Nawabi J, Payabvash S. A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence. Bioengineering 2024, 11: 1274. DOI: 10.3390/bioengineering11121274.Peer-Reviewed Original ResearchNon-contrast head computed tomographyPerihematomal edemaHead computed tomographyIntracerebral hemorrhageComputed tomographyVolume similarityUniversity Medical Center Hamburg-EppendorfSecondary brain injuryYale cohortInfratentorial locationMulticentre trialCT scanTreatment planningNon-contrastHamburg-EppendorfImaging markersHemorrhagic strokeHemorrhageEdemaCohortBrain injuryDice coefficientCUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Liu C, Amodio M, Shen L, Gao F, Avesta A, Aneja S, Wang J, Del Priore L, Krishnaswamy S. CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation. Lecture Notes In Computer Science 2024, 15008: 155-165. DOI: 10.1007/978-3-031-72111-3_15.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationLack of labeled dataUnsupervised deep learning frameworkSegmenting medical imagesDeep learning frameworkBrain MRI imagesRetinal fundus imagesContrastive learningLearning frameworkUnsupervised methodDeep learningExpert annotationsData topologyMedical imagesGranularity levelsEmbedding mapHausdorff distanceFundus imagesDice coefficientImage dataEmbeddingAnnotationLearningMRI images