AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?
By Jun Ma, Yao Zhang, Song Gu, Yichi Zhang, Cheng Zhu, Qiyuan Wang, Xin Liu, Xingle An, Cheng Ge, Shucheng Cao and 7 more. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
479 citations recorded by the source corpus.
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.
Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/8aefc1469ba378389e1742340d25a24ba21a94a6
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Citations in the corpus
- Segment Anything in Medical Images (2023, background): “, T1, T2, ADC, FLAIR) [24][10][6][41][5][8][29][42], abdomen tumor, COVID-19 infection, gallbladder, head and neck tumor, liver, pancreas, pleural effusion, and stomach segmentation in CT images [34][19][7][33][32][42][4][28][27][12], breast tumor, liver, and vessel segmentation in ultrasound images”
- Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions (2024, background): “Accurate segmentation can provide reliable volumetric and shape information of target structures, thereby assisting in many further clinical applications like disease diagnosis, quantitative analysis, and surgical planning [3, 4, 5].”
- Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “This limitation is also highlighted by recent semisupervised medical segmentation benchmarks like [133] and”
- AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation (2022, methodology): “Abdomen Organ Dataset #Organs #Scans #Slices # Anns per Scan Modality Region Year MSD-Liver† [1] 1 201 29,402 3410K CT France 2018 MSD-Spleen† [1] 1 61 1,563 40K CT United States 2018 MSD-Prostate† [1] 1 48 712 15K MRI Netherlands 2018 MSD-Pancreas† [1] 1 420 13,141 144K CT United States 2018 Kits† ”