![]() ![]() We have previously developed a user-friendly software tool for image-to-image translation using deep learning (DeepImageTranslator, described in, released at: ). Nevertheless, most deep learning pipelines for semantic image segmentation generate color-coded segmentation maps stored as image files, while most free software programs for medical image analysis ( e.g., 3D-Slicer, OsiriX Lite, and AMIDE) cannot use these files to generate ROI statistics of multimodal images stored as DICOM files. One possible method is the use of deep learning for automated segmentation. However, for organs/tissues with complex shapes ( e.g., the intestines and adipose tissues), manual ROI segmentation is not a scalable approach. The use of spherical or ellipsoid ROIs may be sufficient for large organs such as the liver and large muscle groups. Analysis of multimodal medical images ( e.g., position emission tomography/magnetic resonance imaging and PET/computed tomography ) often requires the selection of one or many anatomical regions of interest (ROIs) for extraction of useful statistics. ![]()
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