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Kuznetsova A, Rom H, Alldrin N, et al. The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale[J]. International Journal of Computer Vision, 2020, 128(7): 1956-1981
1981
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Milletari F, Navab N, Ahmadi S A. V-net: Fully convolutional neural networks for volumetric medical image segmentation[C]//2016 fourth international conference on 3D vision (3DV). Ieee, 2016: 565-571
2016
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Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2980-2988
2017
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Bernard O, Lalande A, Zotti C, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?[J]. IEEE transactions on medical imaging, 2018, 37(11): 2514-2525
2018
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Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020
Original
2020
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Caron M, Touvron H, Misra I, et al. Emerging properties in self-supervised vision transformers[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 9650-9660
2021
Earlier work this paper cites.
Radford A, Kim J W, Hallacy C, et al. Learning transferable visual models from natural language supervision[C]//International conference on machine learning. PMLR, 2021: 8748-8763
2021
Earlier work this paper cites.
Jia C, Yang Y, Xia Y, et al. Scaling up visual and vision-language representation learning with noisy text supervision[C]//International conference on machine learning. PMLR, 2021: 4904-4916
2021
Earlier work this paper cites.
Ramesh A, Pavlov M, Goh G, et al. Zero-shot text-to-image generation[C]//International Conference on Machine Learning. PMLR, 2021: 8821-8831
2021
Earlier work this paper cites.
Shapey J, Kujawa A, Dorent R, et al. Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm[J]. Scientific Data, 2021, 8(1): 286
2021
Earlier work this paper cites.
Cheng J, Tian S, Yu L, et al. ResGANet: Residual group attention network for medical image classification and segmentation[J]. Medical Image Analysis, 2022, 76: 102313
2022
Earlier work this paper cites.
Aljabri M, AlGhamdi M. A review on the use of deep learning for medical images segmentation[J]. Neurocomputing, 2022
2022
Earlier work this paper cites.
Xie X, Pan X, Shao F, et al. Mci-net: multi-scale context integrated network for liver ct image segmentation[J]. Computers and Electrical Engineering, 2022, 101: 108085
2022
Earlier work this paper cites.
Ma J, Zhang Y, Gu S, et al. Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge[J]. Medical Image Analysis, 2022, 82: 102616
2022
Earlier work this paper cites.
Liu Q, Xu Z, Bertasius G, et al. SimpleClick: Interactive image segmentation with simple vision transformers[J]. arXiv preprint arXiv:2210.11006, 2022
Original
2022
Earlier work this paper cites.

Similar

Chen Z, Duan Y, Wang W, et al. Vision transformer adapter for dense predictions[J]. arXiv preprint arXiv:2205.08534, 2022
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2022
Cited alongside, same era.
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Original
2023
Cited alongside, same era.
Wang X, Zhang X, Cao Y, et al. Seggpt: Segmenting everything in context[J]. arXiv preprint arXiv:2304.03284, 2023
Original
2023
Cited alongside, same era.
Kirillov A, Mintun E, Ravi N, et al. Segment anything[J]. arXiv preprint arXiv:2304.02643, 2023
Original
2023
Cited alongside, same era.
Tang L, Xiao H, Li B. Can sam segment anything? when sam meets camouflaged object detection[J]. arXiv preprint arXiv:2304.04709, 2023

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2023

SAM-Med2D

Cheng, Junlong, Ye, Jin, Deng, Zhongying et al.

Understand

The Segment Anything Model (SAM) represents a state-of-the-art research advancement in natural image segmentation, achieving impressive results with input prompts such as points and bounding boxes.

However, our evaluation and recent research indicate that directly applying the pretrained SAM to medical image segmentation does not yield satisfactory performance.
This limitation primarily arises from significant domain gap between natural images and medical images.
To bridge this gap, we introduce SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.

Built on

Kuznetsova A, Rom H, Alldrin N, et al. The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale[J]. International Journal of Computer Vision, 2020, 128(7): 1956-1981
1981
Earlier work this paper cites.
Milletari F, Navab N, Ahmadi S A. V-net: Fully convolutional neural networks for volumetric medical image segmentation[C]//2016 fourth international conference on 3D vision (3DV). Ieee, 2016: 565-571
2016
Earlier work this paper cites.
Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2980-2988
2017
Earlier work this paper cites.
Bernard O, Lalande A, Zotti C, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?[J]. IEEE transactions on medical imaging, 2018, 37(11): 2514-2525
2018
Earlier work this paper cites.
Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020
Original
2020
Earlier work this paper cites.
Caron M, Touvron H, Misra I, et al. Emerging properties in self-supervised vision transformers[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 9650-9660
2021
Earlier work this paper cites.
Radford A, Kim J W, Hallacy C, et al. Learning transferable visual models from natural language supervision[C]//International conference on machine learning. PMLR, 2021: 8748-8763
2021
Earlier work this paper cites.
Jia C, Yang Y, Xia Y, et al. Scaling up visual and vision-language representation learning with noisy text supervision[C]//International conference on machine learning. PMLR, 2021: 4904-4916
2021
Earlier work this paper cites.
Ramesh A, Pavlov M, Goh G, et al. Zero-shot text-to-image generation[C]//International Conference on Machine Learning. PMLR, 2021: 8821-8831
2021
Earlier work this paper cites.
Shapey J, Kujawa A, Dorent R, et al. Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm[J]. Scientific Data, 2021, 8(1): 286
2021
Earlier work this paper cites.
Cheng J, Tian S, Yu L, et al. ResGANet: Residual group attention network for medical image classification and segmentation[J]. Medical Image Analysis, 2022, 76: 102313
2022
Earlier work this paper cites.
Aljabri M, AlGhamdi M. A review on the use of deep learning for medical images segmentation[J]. Neurocomputing, 2022
2022
Earlier work this paper cites.
Xie X, Pan X, Shao F, et al. Mci-net: multi-scale context integrated network for liver ct image segmentation[J]. Computers and Electrical Engineering, 2022, 101: 108085
2022
Earlier work this paper cites.
Ma J, Zhang Y, Gu S, et al. Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge[J]. Medical Image Analysis, 2022, 82: 102616
2022
Earlier work this paper cites.
Liu Q, Xu Z, Bertasius G, et al. SimpleClick: Interactive image segmentation with simple vision transformers[J]. arXiv preprint arXiv:2210.11006, 2022
Original
2022
Earlier work this paper cites.

Similar

Chen Z, Duan Y, Wang W, et al. Vision transformer adapter for dense predictions[J]. arXiv preprint arXiv:2205.08534, 2022
Original
2022
Cited alongside, same era.
Cheng J, Gao C, Wang F, et al. SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks[J]. arXiv preprint arXiv:2307.02953, 2023
Original
2023
Cited alongside, same era.
Wang X, Zhang X, Cao Y, et al. Seggpt: Segmenting everything in context[J]. arXiv preprint arXiv:2304.03284, 2023
Original
2023
Cited alongside, same era.
Kirillov A, Mintun E, Ravi N, et al. Segment anything[J]. arXiv preprint arXiv:2304.02643, 2023
Original
2023
Cited alongside, same era.
Tang L, Xiao H, Li B. Can sam segment anything? when sam meets camouflaged object detection[J]. arXiv preprint arXiv:2304.04709, 2023

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2023

Cited alongside, same era.

Ji G P, Fan D P, Xu P, et al. SAM Struggles in Concealed Scenes–Empirical Study on" Segment Anything"[J]. arXiv preprint arXiv:2304.06022, 2023

Original

2023

Cited alongside, same era.

He S, Bao R, Li J, et al. Accuracy of segment-anything model (sam) in medical image segmentation tasks[J]. arXiv preprint arXiv:2304.09324, 2023

Original

2023

Cited alongside, same era.

Zhou T, Zhang Y, Zhou Y, et al. Can sam segment polyps?[J]. arXiv preprint arXiv:2304.07583, 2023

Original

2023

Cited alongside, same era.

Putz F, Grigo J, Weissmann T, et al. The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning[J]. arXiv preprint arXiv:2304.07875, 2023

Original

2023

Cited alongside, same era.

Mazurowski M A, Dong H, Gu H, et al. Segment anything model for medical image analysis: an experimental study[J]. Medical Image Analysis, 2023: 102918

2023

Cited alongside, same era.

Huang Y, Yang X, Liu L, et al. Segment anything model for medical images?[J]. arXiv preprint arXiv:2304.14660, 2023

Original

2023

Cited alongside, same era.

Deng G, Zou K, Ren K, et al. SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image[J]. arXiv preprint arXiv:2307.04973, 2023

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2023

Cited alongside, same era.

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Closest in time.

Cheng D, Qin Z, Jiang Z, et al. Sam on medical images: A comprehensive study on three prompt modes[J]. arXiv preprint arXiv:2305.00035, 2023

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Heller N, Isensee F, Trofimova D, et al. The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT[J]. arXiv preprint arXiv:2307.01984, 2023

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Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation: MICCAI 2022 Challenge, FLARE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings[M]. Springer Nature, 2023

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