Titlebook: Clinical Image-Based Procedures; 13th International W Klaus Drechsler,Cristina Oyarzun Laura,Marius Erdt Conference proceedings 2024 The Ed

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Palgrave Studies in the History of Genocidetermine the wound’s area. Our method estimates %TBSA with an average error of 8.5%, which is a huge improvement over the 140% error produced by existing methods. Our method is also easily accessible since it uses commercial-off-the-shelf (COTS) devices. This makes it practical for anyone, even those

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Palgrave Studies in the History of Genocidemultimodal images while preserving anatomical details. Evaluation on 70 CBCT and 70 MRI scans acquired at the same time points for 70 TM DJD patients demonstrates robustness to variations in image quality, anatomical morphology, and acquisition protocols. By integrating MRI soft tissue information w

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Palgrave Studies in the History of Genocideon, liver capsule and hepatic vessel segmentation, and quantitative similarity measurement. We used a pretrained EfficientNet-B3 network for liver view classification, achieving 99% accuracy. We then used Efficient-UNet to segment the liver capsule and hepatic vessels, obtaining Dice scores of 90% a

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,CloverNet – Leveraging Planning Annotations for Enhanced Procedural MR Segmentation: An Applicationof MP-Fx. CloverNet improves the absolute Dice Score by 3.73% (relative +4.34%, p<0.001) when compared with conventional 3D UNet. Moreover, we believe this approach is potentially applicable to other longitudinal use cases in which a prior segmentation of the ROI is available.

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,Enhancing Image Classification in Small and Unbalanced Datasets Through Synthetic Data Augmentationated to train and validate an automatic method for assessing the quality of cleanliness of esophagogastroduodenoscopy images..By combining real and synthetic data, an increase of over 18% in the accuracy of the most challenging underrepresented class was observed. The proposed strategy not only bene

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,Automated Multi-View Planning for Endovascular Aneurysm Repair Procedures,based on the centerlines surrounding this bifurcation, where we regard the estimated 3D local plane as a good view plane in patient physical space. Next, some 3D points are collected from these centerlines, projected onto the estimated local 3D plane, and transformed to the image domain to get the p