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.

翻译：跨模态图像配准（IMIR）及基于腹部超声数据的图像分割具有重要的临床应用价值，包括图像引导手术、自动器官测量及机器人导航。然而，由于公共数据集的匮乏，相关研究受到严重制约。本文提出TRUSTED数据集（三维肾脏超声与断层扫描配对数据集），包含48例人类患者（96个肾脏）的配对经腹三维超声与CT肾脏图像，涵盖由两名资深放射技师标注的分割图像及解剖标志点。评分者间分割一致性超过94%（Dice系数），并采用STAPLE算法生成金标准分割结果。数据集标注了七个对IMIR系统开发与评估至关重要的解剖标志点。为验证数据集实用性，对5种竞争性深度学习模型在自动肾脏分割任务中进行基准测试，CT与超声图像的平均Dice评分分别为83.2%~89.1%和61.9%~79.4%。对三种IMIR方法进行基准评估，其中相干点漂移算法表现最优，平均目标配准误差为4.53mm。TRUSTED数据集可免费供研究者开发并验证新型分割与IMIR方法。