mlsanityData to 14 Oct 2026

TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research

arXiv 2310.12646 ↗First posted 19 Oct 2023eess.IV cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 21 of its 61 references that are in the map. First posted on arXiv 19 Oct 2023.

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21 of 61 references.

References · 21

  1. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  2. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
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  5. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
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  10. 2017Quicksilver: Fast Predictive Image Registration - a Deep Learning Approachcited by 113
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  12. 2019PCRNet: Point Cloud Registration Network using PointNet Encodingcited by 63
9 more
  1. 2017Label-driven weakly-supervised learning for multimodal deformable image registrationcited by 34
  2. 2018Evaluating the Impact of Intensity Normalization on MR Image Synthesiscited by 26
  3. 2018Learning Deep Similarity Metric for 3D MR-TRUS Registrationcited by 24
  4. 2021COTR: Convolution in Transformer Network for End to End Polyp Detectioncited by 19
  5. 2022The Open Kidney Ultrasound Data Setcited by 18
  6. 2022Global Multi-modal 2D/3D Registration via Local Descriptors Learningcited by 13
  7. 2018Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networkscited by 5
  8. 2022Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentationcited by 5
  9. 2022Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026US-JEPA: A Joint Embedding Predictive Architecture for Ultrasoundcited by 2
  2. 20263D Ultrasound-Derived Pseudo-CT Synthesis Using a Transformer-Augmented Residual Network for Real-Time Operator Guidancecited by 0
  3. 2026Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reductioncited by 0