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

2025-07-29

AI Summary

Current CT anatomical segmentation methods suffer from fragmented models, inconsistent evaluation protocols, and insufficient whole-body coverage—primarily due to the absence of large-scale, highly heterogeneous, multi-structure annotated datasets. To address this, we introduce CADS, the first open-source whole-body anatomical segmentation benchmark, comprising 22,022 CT volumes annotated with 167 anatomical structures—18× larger in scale and 60% richer in anatomical categories than the largest prior dataset. We establish a standardized annotation protocol and an end-to-end toolchain, and train a unified segmentation model (CADS-model) based on architectures including UNet. CADS-model achieves statistically significant improvements over state-of-the-art methods across 18 public benchmarks and a real-world hospital cohort, with validated utility in clinical applications such as radiation oncology. All data, models, and code are publicly released.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Accurate delineation of anatomical structures in volumetric CT scans is crucial for diagnosis and treatment planning. While AI has advanced automated segmentation, current approaches typically target individual structures, creating a fragmented landscape of incompatible models with varying performance and disparate evaluation protocols. Foundational segmentation models address these limitations by providing a holistic anatomical view through a single model. Yet, robust clinical deployment demands comprehensive training data, which is lacking in existing whole-body approaches, both in terms of data heterogeneity and, more importantly, anatomical coverage. In this work, rather than pursuing incremental optimizations in model architecture, we present CADS, an open-source framework that prioritizes the systematic integration, standardization, and labeling of heterogeneous data sources for whole-body CT segmentation. At its core is a large-scale dataset of 22,022 CT volumes with complete annotations for 167 anatomical structures, representing a significant advancement in both scale and coverage, with 18 times more scans than existing collections and 60% more distinct anatomical targets. Building on this diverse dataset, we develop the CADS-model using established architectures for accessible and automated full-body CT segmentation. Through comprehensive evaluation across 18 public datasets and an independent real-world hospital cohort, we demonstrate advantages over SoTA approaches. Notably, thorough testing of the model's performance in segmentation tasks from radiation oncology validates its direct utility for clinical interventions. By making our large-scale dataset, our segmentation models, and our clinical software tool publicly available, we aim to advance robust AI solutions in radiology and make comprehensive anatomical analysis accessible to clinicians and researchers alike.

Problem

• Lack of comprehensive whole-body CT segmentation datasets
• Fragmented incompatible models for anatomical structure segmentation
• Need for standardized robust clinical deployment solutions

Innovation

• Large-scale dataset with 167 anatomical structures
• Open-source framework for whole-body CT segmentation
• Comprehensive evaluation across 18 public datasets