  Inter-modal image registration (IMIR) and image segmentation with abdominal
Ultrasound (US) data has 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 Sound TomodEnsitometrie
Dataset), comprising paired transabdominal 3DUS and CT kidney images from 48
human patients (96 kidneys), including segmentation, and anatomical landmark
annotations by two experienced radiographers. Inter-rater segmentation
agreement was over 94 (Dice score), and gold-standard segmentations were
generated using the STAPLE algorithm. Seven anatomical landmarks were
annotated, important for IMIR systems development and evaluation. To validate
the dataset's utility, 5 competitive Deep Learning models for automatic kidney
segmentation were benchmarked, yielding average DICE scores from 83.2% to 89.1%
for CT, and 61.9% to 79.4% for US images. Three IMIR methods were benchmarked,
and Coherent Point Drift performed best with an average Target Registration
Error of 4.53mm. The TRUSTED dataset may be used freely researchers to develop
and validate new segmentation and IMIR methods.
