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New CADS dataset enables comprehensive whole-body CT segmentation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced CADS, a comprehensive dataset and segmentation framework designed for whole-body anatomy in computed tomography (CT) scans. This new resource harmonizes anatomical labels from over 100 imaging centers, providing annotations for 167 structures across 22,022 CT volumes. CADS utilizes a self-training approach with shape-informed quality control to generate high-quality pseudo-labels for sparsely annotated scans. The framework demonstrates robust segmentation performance across distribution shifts and outperforms existing state-of-the-art methods on 18 public datasets, with further validation in an independent oncology cohort. AI

IMPACT This dataset and framework could accelerate research in computational anatomy and improve diagnostic accuracy in medical imaging.

RANK_REASON The item is a research paper detailing a new dataset and framework for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CADS dataset enables comprehensive whole-body CT segmentation

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The item is a research paper detailing a new dataset and framework for medical image analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Murong Xu, Fernando Navarro, Maksym Fritsak, Ibrahim Ethem Hamamci, Suprosanna Shit, Bastian Wittmann, Sezgin Er, Sebastian M. Christ, Ezequiel de la Rosa, Julian Deseoe, Robert Graf, Hendrik M\"oller, Anjany Sekuboyina, Jan C. Peeken, Sven Becker, Giuli… ·

    CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography

    arXiv:2507.22953v2 Announce Type: replace-cross Abstract: Volumetric computed tomography (CT) segmentation supports quantitative image analysis, but most AI-based methods remain organ-specific, with fragmented models, inconsistent label definitions, and heterogeneous training and…

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