DescriptionVolumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the artery. In this work, we introduce a hybrid approach by combining a deep learning method with a consolidated interaction technique. In particular, we trained a 2D and 3D U-net on a limited number of patches extracted from 25 labeled CTA scans. Afterwards, we use an interactive approach, which consists in defining a region of interest (ROI) by just placing a seed point. This seed point is later used as the center of a 2D or 3D patch to be fed to the 2D or 3D U-net, respectively. Due to the low content variation of these patches, this method allows to correctly segment the ROIs without the need for parameter tuning for each dataset and with a smaller training dataset, requiring the same minimal interaction as state-of-the-art interactive methods.
|Period||28 Feb 2020|
|Event title||SPIE MEDICAL IMAGING 2020|
|Location||Houston, United States, Texas|
|Degree of Recognition||International|