SAM on Medical Images: A Comprehensive Study on Three Prompt Modes

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J. Silva, A. Histace, O. Romain, X. Dray, and B. Granado, “Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,” International journal of computer assisted radiology and surgery , vol. 9, no. 2, pp. 283–293, 2014
2014
Earlier work this paper cites.
J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, D. Gil, C. Rodríguez, and F. Vilariño, “Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,” Computerized medical imaging and graphics , vol. 43, pp. 99–111, 2015
2015
Earlier work this paper cites.
M. Boccardi, M. Bocchetta, F. C. Morency, D. L. Collins, M. Nishikawa, R. Ganzola, M. J. Grothe, D. Wolf, A. Redolfi, M. Pievani et al. , “Training labels for hippocampal segmentation based on the eadc-adni harmonized hippocampal protocol,” Alzheimer’s & Dementia , vol. 11, no. 2, pp. 175–183, 2015
2015
Earlier work this paper cites.
N. Tajbakhsh, S. R. Gurudu, and J. Liang, “Automated polyp detection in colonoscopy videos using shape and context information,” IEEE transactions on medical imaging , vol. 35, no. 2, pp. 630–644, 2015
2015
Earlier work this paper cites.
D. Vázquez, J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, A. M. López, A. Romero, M. Drozdzal, and A. Courville, “A benchmark for endoluminal scene segmentation of colonoscopy images,” Journal of healthcare engineering , vol. 2017, 2017
2017
Earlier work this paper cites.
J. Hu, Y. Chen, and Z. Yi, “Automated segmentation of macular edema in oct using deep neural networks,” Medical image analysis , vol. 55, pp. 216–227, 2019
2019
Earlier work this paper cites.
W. Al-Dhabyani, M. Gomaa, H. Khaled, and A. Fahmy, “Dataset of breast ultrasound images,” Data in brief , vol. 28, p. 104863, 2020
2020
Earlier work this paper cites.
D.-P. Fan, G.-P. Ji, T. Zhou, G. Chen, H. Fu, J. Shen, and L. Shao, “Pranet: Parallel reverse attention network for polyp segmentation,” in International conference on medical image computing and computer-assisted intervention . Springer, 2020, pp. 263–273
2020
Earlier work this paper cites.
D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. d. Lange, D. Johansen, and H. D. Johansen, “Kvasir-seg: A segmented polyp dataset,” in International Conference on Multimedia Modeling . Springer, 2020, pp. 451–462
2020
Earlier work this paper cites.
A. M. Tahir, M. E. Chowdhury, A. Khandakar, T. Rahman, Y. Qiblawey, U. Khurshid, S. Kiranyaz, N. Ibtehaz, M. S. Rahman, S. Al-Maadeed, S. Mahmud, M. Ezeddin, K. Hameed, and T. Hamid, “Covid-19 infection localization and severity grading from chest x-ray images,” Computers in Biology and Medicine , vol. 139, p. 105002, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0010482521007964
2021
Earlier work this paper cites.
D. Kuzinkovas and S. Clement, “The detection of covid-19 in chest x-rays using ensemble cnn techniques,” medRxiv , pp. 2022–11, 2022
2022
Earlier work this paper cites.
C. Wang, A. Mahbod, I. Ellinger, A. Galdran, S. Gopalakrishnan, J. Niezgoda, and Z. Yu, “Fuseg: The foot ulcer segmentation challenge,” 2022
2022
Earlier work this paper cites.

Similar

P. Bilic, P. Christ, H. B. Li, E. Vorontsov, A. Ben-Cohen, G. Kaissis, A. Szeskin, C. Jacobs, G. E. H. Mamani, G. Chartrand et al. , “The liver tumor segmentation benchmark (lits),” Medical Image Analysis , vol. 84, p. 102680, 2023
2023
Cited alongside, same era.
J. Cen, Z. Zhou, J. Fang, W. Shen, L. Xie, X. Zhang, and Q. Tian, “Segment anything in 3d with nerfs,” 2023
2023
Cited alongside, same era.
T. Chen, L. Zhu, C. Ding, R. Cao, S. Zhang, Y. Wang, Z. Li, L. Sun, P. Mao, and Y. Zang, “Sam fails to segment anything?–sam-adapter: Adapting sam in underperformed scenes: Camouflage, shadow, and more,” arXiv preprint arXiv:2304.09148 , 2023
Original
2023
Cited alongside, same era.
R. Deng, C. Cui, Q. Liu, T. Yao, L. W. Remedios, S. Bao, B. A. Landman, L. E. Wheless, L. A. Coburn, K. T. Wilson et al. , “Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,” arXiv preprint arXiv:2304.04155 , 2023
Original
2023
Cited alongside, same era.

Then

Z. Ma, X. Hong, and Q. Shangguan, “Can sam count anything? an empirical study on sam counting,” 2023
2023
Closest in time.
M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, and Y. Zhang, “Segment anything model for medical image analysis: an experimental study,” arXiv preprint arXiv:2304.10517 , 2023
Original
2023
Closest in time.
S. Mohapatra, A. Gosai, and G. Schlaug, “Brain extraction comparing segment anything model (sam) and fsl brain extraction tool,” arXiv preprint arXiv:2304.04738 , 2023
Original
2023
Closest in time.
F. Putz, J. Grigo, T. Weissmann, P. Schubert, D. Hoefler, A. Gomaa, H. B. Tkhayat, A. Hagag, S. Lettmaier, B. Frey et al. , “The segment anything foundation model achieves favorable brain tumor autosegmentation accuracy on mri to support radiotherapy treatment planning,” arXiv preprint arXiv:2304.07875 , 2023
Original
2023
Closest in time.

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SAM on Medical Images: A Comprehensive Study on Three Prompt Modes · Around

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J. Silva, A. Histace, O. Romain, X. Dray, and B. Granado, “Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer,” International journal of computer assisted radiology and surgery , vol. 9, no. 2, pp. 283–293, 2014
2014
Earlier work this paper cites.
J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, D. Gil, C. Rodríguez, and F. Vilariño, “Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians,” Computerized medical imaging and graphics , vol. 43, pp. 99–111, 2015
2015
Earlier work this paper cites.
M. Boccardi, M. Bocchetta, F. C. Morency, D. L. Collins, M. Nishikawa, R. Ganzola, M. J. Grothe, D. Wolf, A. Redolfi, M. Pievani et al. , “Training labels for hippocampal segmentation based on the eadc-adni harmonized hippocampal protocol,” Alzheimer’s & Dementia , vol. 11, no. 2, pp. 175–183, 2015
2015
Earlier work this paper cites.
N. Tajbakhsh, S. R. Gurudu, and J. Liang, “Automated polyp detection in colonoscopy videos using shape and context information,” IEEE transactions on medical imaging , vol. 35, no. 2, pp. 630–644, 2015
2015
Earlier work this paper cites.
D. Vázquez, J. Bernal, F. J. Sánchez, G. Fernández-Esparrach, A. M. López, A. Romero, M. Drozdzal, and A. Courville, “A benchmark for endoluminal scene segmentation of colonoscopy images,” Journal of healthcare engineering , vol. 2017, 2017
2017
Earlier work this paper cites.
J. Hu, Y. Chen, and Z. Yi, “Automated segmentation of macular edema in oct using deep neural networks,” Medical image analysis , vol. 55, pp. 216–227, 2019
2019
Earlier work this paper cites.
W. Al-Dhabyani, M. Gomaa, H. Khaled, and A. Fahmy, “Dataset of breast ultrasound images,” Data in brief , vol. 28, p. 104863, 2020
2020
Earlier work this paper cites.
D.-P. Fan, G.-P. Ji, T. Zhou, G. Chen, H. Fu, J. Shen, and L. Shao, “Pranet: Parallel reverse attention network for polyp segmentation,” in International conference on medical image computing and computer-assisted intervention . Springer, 2020, pp. 263–273
2020
Earlier work this paper cites.
D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. d. Lange, D. Johansen, and H. D. Johansen, “Kvasir-seg: A segmented polyp dataset,” in International Conference on Multimedia Modeling . Springer, 2020, pp. 451–462
2020
Earlier work this paper cites.
A. M. Tahir, M. E. Chowdhury, A. Khandakar, T. Rahman, Y. Qiblawey, U. Khurshid, S. Kiranyaz, N. Ibtehaz, M. S. Rahman, S. Al-Maadeed, S. Mahmud, M. Ezeddin, K. Hameed, and T. Hamid, “Covid-19 infection localization and severity grading from chest x-ray images,” Computers in Biology and Medicine , vol. 139, p. 105002, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0010482521007964
2021
Earlier work this paper cites.
D. Kuzinkovas and S. Clement, “The detection of covid-19 in chest x-rays using ensemble cnn techniques,” medRxiv , pp. 2022–11, 2022
2022
Earlier work this paper cites.
C. Wang, A. Mahbod, I. Ellinger, A. Galdran, S. Gopalakrishnan, J. Niezgoda, and Z. Yu, “Fuseg: The foot ulcer segmentation challenge,” 2022
2022
Earlier work this paper cites.

Similar

P. Bilic, P. Christ, H. B. Li, E. Vorontsov, A. Ben-Cohen, G. Kaissis, A. Szeskin, C. Jacobs, G. E. H. Mamani, G. Chartrand et al. , “The liver tumor segmentation benchmark (lits),” Medical Image Analysis , vol. 84, p. 102680, 2023
2023
Cited alongside, same era.
J. Cen, Z. Zhou, J. Fang, W. Shen, L. Xie, X. Zhang, and Q. Tian, “Segment anything in 3d with nerfs,” 2023
2023
Cited alongside, same era.
T. Chen, L. Zhu, C. Ding, R. Cao, S. Zhang, Y. Wang, Z. Li, L. Sun, P. Mao, and Y. Zang, “Sam fails to segment anything?–sam-adapter: Adapting sam in underperformed scenes: Camouflage, shadow, and more,” arXiv preprint arXiv:2304.09148 , 2023
Original
2023
Cited alongside, same era.
R. Deng, C. Cui, Q. Liu, T. Yao, L. W. Remedios, S. Bao, B. A. Landman, L. E. Wheless, L. A. Coburn, K. T. Wilson et al. , “Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,” arXiv preprint arXiv:2304.04155 , 2023
Original
2023
Cited alongside, same era.

Then

Z. Ma, X. Hong, and Q. Shangguan, “Can sam count anything? an empirical study on sam counting,” 2023
2023
Closest in time.
M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, and Y. Zhang, “Segment anything model for medical image analysis: an experimental study,” arXiv preprint arXiv:2304.10517 , 2023
Original
2023
Closest in time.
S. Mohapatra, A. Gosai, and G. Schlaug, “Brain extraction comparing segment anything model (sam) and fsl brain extraction tool,” arXiv preprint arXiv:2304.04738 , 2023
Original
2023
Closest in time.
F. Putz, J. Grigo, T. Weissmann, P. Schubert, D. Hoefler, A. Gomaa, H. B. Tkhayat, A. Hagag, S. Lettmaier, B. Frey et al. , “The segment anything foundation model achieves favorable brain tumor autosegmentation accuracy on mri to support radiotherapy treatment planning,” arXiv preprint arXiv:2304.07875 , 2023
Original
2023
Closest in time.

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