AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

By Yuanfeng Ji, Haotian Bai, Jie Yang, Chongjian Ge, Ye Zhu, Ruimao Zhang, Zhuguo Li, Lingyan Zhang, Wanling Ma, Xiang Wan and 1 more. Neural Information Processing Systems, 2022

574 citations recorded by the source corpus.

Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. Information can be found at https://amos22.grand-challenge.org.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/27d5abc68c2e5555bca47a3aff3f074b01f35b8c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Alan L. Yuille
2. Jun Ma
3. Bo Wang
4. Jieneng Chen
5. Yanwu Xu

In Computer Vision and Pattern Recognition

1. Jieneng Chen
2. Yanwu Xu
3. Jie Liu
4. Yixuan Yuan
5. Zongwei Zhou
6. Jun Ma
7. Bo Wang
8. Yucheng Tang

In Radiology, Nuclear Medicine and Imaging

1. Alan L. Yuille
2. Huihui Fang
3. Junde Wu
4. Yu Zhang
5. Yanwu Xu
6. Suncheng Xiang
7. Jiacheng Ruan

Citations in the corpus

• MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation (2023, unknown): “…demonstrate the benefits of the MedNeXt architecture – 1) Beyond-the-Cranial-Vault (BTCV) Abdominal CT Organ Segmentation [16], 2) AMOS22 Abdominal CT Organ Segmentation [14] 3) Kidney Tumor Segmentation Challenge 2019 Dataset (KiTS19) [11], 4) Brain Tumor Segmentation Challenge 2021 (BraTS21) [1].”
• U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation (2024, methodology): “Abdomen MRI: This dataset was from the MICCAI 2022 AMOS Challenge [25] that also focused on abdominal organ segmentation.”
• Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining (2024, unknown): “We evaluate the performance and scalability of Swin-UMamba across three distinct medical image segmentation datasets, encompassing AbdomenMRI [12] (abdominal organs), Endoscopy [1] (instruments), and Microscopy [19] (cell).”
• MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer (2023, methodology): “We use public AMOS2022 [12] dataset, which we employ 200 multi-contrast abdominal CT from AMOS 2022 with sixteen anatomies manually annotated for abdominal multi-organ segmentation.”
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation (2023, unknown): “We used two datasets, AMOS2022 [20] and BTCV [11], both publicly available and annotated with sixteen and twelve anatomies, respectively.”
• CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection (2023, background): “AMOS22 [28] 15 500 Spl, RKid, LKid, Gall, Eso, Liv, Sto, Aor, IVC, Pan, RAG, LAG, Duo, Bla, Pro/UTE 14.”
• Segment Anything in Medical Images (2023, background): “…gallbladder, heart left and right ventricle, liver, pancreas, and prostate segmentation in various MR sequences (e.g., T1, T2, ADC, FLAIR) [24] [12], breast tumor, liver, and vessel segmentation in ultrasound images [2][48], heart and lungs segmentation in X-Ray images [40], polyp and…”
• An OpenMind for 3D Medical Vision Self-supervised Learning (2024, unknown): “AMOS22 (AMO) [38]: CT and MRI scans of 15 abdominal organs.”