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Found 6 papers, 0 papers with code
Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation
Validation results on 11 patients showed that the accuracy of training labels was statistically significantly improved, with the Dice similarity coefficient of muscle, subcutaneous and visceral adipose tissue increased from 74. 2% to 91. 5%, 91. 2% to 95. 6%, and 77. 6% to 88. 5%, respectively (p<0. 05).
Automated Plaque Detection and Agatston Score Estimation on Non-Contrast CT Scans: A Multicenter Study
Heart, aorta, and lung segmentations were determined using TotalSegmentator, while plaques in the coronary arteries and heart valves were manually labeled for 801 volumes.
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis.
Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CT
Pheochromocytomas and Paragangliomas (PPGLs) are rare adrenal and extra-adrenal tumors which have the potential to metastasize.
Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study
This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool.
Automated Measurement of Pericoronary Adipose Tissue Attenuation and Volume in CT Angiography
In this pilot work, we developed a fully automated approach for the measurement of PCAT mean attenuation and volume in the region around both coronary arteries.
