# Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases

arXiv: https://arxiv.org/abs/2406.13674 · HTML: https://mlsanity.com/paper/2406.13674/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)
- [LViT: Language meets Vision Transformer in Medical Image Segmentation](https://mlsanity.com/paper/2206.14718/) (2022)
- [3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2209.15076/) (2022)
- [Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision](https://mlsanity.com/paper/2203.02106/) (2022)
- [Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge](https://mlsanity.com/paper/2308.05862/) (2023)
- [Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation](https://mlsanity.com/paper/2107.09842/) (2021)
- [ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding](https://mlsanity.com/paper/2307.16226/) (2023)
- [TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation](https://mlsanity.com/paper/2207.03450/) (2022)
- [Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image Segmentation](https://mlsanity.com/paper/2303.05785/) (2023)
- [Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment](https://mlsanity.com/paper/2303.00279/) (2023)
- [HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery](https://mlsanity.com/paper/2303.07717/) (2023)

## Built on by (most cited first, 19 of 19)

- [GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation](https://mlsanity.com/paper/2501.12844/) (2025)
- [MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset](https://mlsanity.com/paper/2503.13560/) (2025)
- [MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation](https://mlsanity.com/paper/2506.18679/) (2025)
- [MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation](https://mlsanity.com/paper/2508.11032/) (2025)
- [Medverse: A Universal Model for Full-Resolution 3D Medical Image Segmentation, Transformation and Enhancement](https://mlsanity.com/paper/2509.09232/) (2025)
- [RAU: Reference-based Anatomical Understanding with Vision Language Models](https://mlsanity.com/paper/2509.22404/) (2025)
- [Beyond Adapter Retrieval: Latent Geometry-Preserving Composition via Sparse Task Projection](https://mlsanity.com/paper/2410.09908/) (2024)
- [Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images](https://mlsanity.com/paper/2410.12831/) (2024)
- [Unified Medical Image Segmentation with State Space Modeling Snake](https://mlsanity.com/paper/2507.12760/) (2025)
- [Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis](https://mlsanity.com/paper/2509.19711/) (2025)
- [Auditing Significance, Metric Choice, and Demographic Fairness in Medical AI Challenges](https://mlsanity.com/paper/2512.19091/) (2025)
- [OWT: A Foundational Organ-Wise Tokenization Framework for Medical Imaging](https://mlsanity.com/paper/2505.04899/) (2025)
- [Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning](https://mlsanity.com/paper/2510.05899/) (2025)
- [HiPerformer: A High-Performance Global-Local Segmentation Model with Modular Hierarchical Fusion Strategy](https://mlsanity.com/paper/2509.20280/) (2025)
- [An Enhanced Pyramid Feature Network Based on Long-Range Dependencies for Multi-Organ Medical Image Segmentation](https://mlsanity.com/paper/2509.24358/) (2025)
- [UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation](https://mlsanity.com/paper/2607.12896/) (2026)
- [TEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake](https://mlsanity.com/paper/2608.17421/) (2026)
- [Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shift](https://mlsanity.com/paper/2608.18193/) (2026)
- [Preserving Anatomical Continuity: Three-Stage Pipeline for Colon Segmentation in 3D Abdominal CT Scans](https://mlsanity.com/paper/2610.03467/) (2026)

## Abstract (arXiv)

Deep learning has enabled great strides in abdominal multi-organ segmentation, even surpassing junior oncologists on common cases or organs. However, robustness on corner cases and complex organs remains a challenging open problem for clinical adoption. To investigate model robustness, we collected and annotated the RAOS dataset comprising 413 CT scans (~80k 2D images, ~8k 3D organ annotations) from 413 patients each with 17 (female) or 19 (male) labelled organs, manually delineated by oncologists. We grouped scans based on clinical information into 1) diagnosis/radiotherapy (317 volumes), 2) partial excision without the whole organ missing (22 volumes), and 3) excision with the whole organ missing (74 volumes). RAOS provides a potential benchmark for evaluating model robustness including organ hallucination. It also includes some organs that can be very hard to access on public datasets like the rectum, colon, intestine, prostate and seminal vesicles. We benchmarked several state-of-the-art methods in these three clinical groups to evaluate performance and robustness. We also assessed cross-generalization between RAOS and three public datasets. This dataset and comprehensive analysis establish a potential baseline for future robustness research: urlhttps://github.com/Luoxd1996/RAOS.