2024
CUTS: 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 imagesThe role of 18F-FDG PET in minimizing variability in gross tumor volume delineation of soft tissue sarcomas
Najem E, Marin T, Zhuo Y, Lahoud R, Tian F, Beddok A, Rozenblum L, Xing F, Moteabbed M, Lim R, Liu X, Woo J, Lostetter S, Lamane A, Chen Y, Ma C, El Fakhri G. The role of 18F-FDG PET in minimizing variability in gross tumor volume delineation of soft tissue sarcomas. Radiotherapy And Oncology 2024, 194: 110186. PMID: 38412906, PMCID: PMC11042980, DOI: 10.1016/j.radonc.2024.110186.Peer-Reviewed Original ResearchGross tumor volume delineationGross tumor volumeDice similarity coefficientF-FDG PET imagingSoft tissue sarcomasInter-reader variabilityGTV delineationRadiation therapy treatment planningF-FDGF-FDG PETTherapy treatment planningPerformance level estimationTumor volume delineationTissue sarcomasPET imagingVolume delineationSimultaneous truthHausdorff distanceDice similarity coefficient scoreAccurate gross tumor volumeImaging modality groupsWilcoxon signed-rank testStatistically significant decreaseSigned-rank testTumor volumeExpert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.
Hoebel K, Bridge C, Ahmed S, Akintola O, Chung C, Huang R, Johnson J, Kim A, Ly K, Chang K, Patel J, Pinho M, Batchelor T, Rosen B, Gerstner E, Kalpathy-Cramer J. Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation. Radiology Artificial Intelligence 2024, 6: e220231. PMID: 38197800, PMCID: PMC10831514, DOI: 10.1148/ryai.220231.Peer-Reviewed Original ResearchConceptsBrain tumor segmentationDeep learning algorithmsSegmentation qualityLearning algorithmsTumor segmentationBrain tumor segmentation algorithmQuantitative quality metricsTumor segmentation algorithmClinical expert evaluationSegmentation performanceAlgorithm evaluationSegmentation algorithmQuality metricsDice scoreHausdorff distanceSegmentation casesAlgorithmExperimental resultsExpert evaluationQuality evaluationMetricsSurvey article
2021
Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas
Marin T, Zhuo Y, Lahoud R, Tian F, Ma X, Xing F, Moteabbed M, Liu X, Grogg K, Shusharina N, Woo J, Lim R, Ma C, Chen Y, El Fakhri G. Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas. Radiotherapy And Oncology 2021, 167: 269-276. PMID: 34808228, PMCID: PMC8934266, DOI: 10.1016/j.radonc.2021.09.034.Peer-Reviewed Original ResearchConceptsGross tumor volumeRadiation therapy treatment planningGross tumor volume contoursGross tumor volume delineationTherapy treatment planningIntra-observer variabilityConsensus contoursGTV contoursPre-operative CT imagesSoft tissue sarcomasRadiation oncologistsTumor volumeBone sarcomasTreatment planningAccurate contoursCT imagesDelineation procedureSarcomaSoft tissueConfidence levelRadiationPatientsHausdorff distanceMultiple contoursX-ray
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