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Qualitative results showing example CT and US segmentations using the TRUSTED dataset (patients 418 and 680). The top two rows show coronal CT slices, with ground-truth segmentations overlaid in blue, and estimated segmentations in green. The bottom two rows show longitudinal US slices, with ground truth segmentations overlaid in red and estimated segmentations in green. The two first columns (Manual Ann.1 and Manual Ann.2) show segmentations from each annotator, and the remaining columns show the best training version on average between single or double target(s) of each automatic segmentation from 4 DNN-based methods.

Qualitative results showing example CT and US segmentations using the TRUSTED dataset (patients 418 and 680). The top two rows show coronal CT slices, with ground-truth segmentations overlaid in blue, and estimated segmentations in green. The bottom two rows show longitudinal US slices, with ground truth segmentations overlaid in red and estimated segmentations in green. The two first columns (Manual Ann.1 and Manual Ann.2) show segmentations from each annotator, and the remaining columns show the best training version on average between single or double target(s) of each automatic segmentation from 4 DNN-based methods.

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Inter-modal image registration (IMIR) and image segmentation with abdominal Ultrasound (US) data have many important clinical applications, including image-guided surgery, automatic organ measurement, and robotic navigation. However, research is severely limited by the lack of public datasets. We propose TRUSTED (the Tridimensional Renal Ultra Soun...