Segment anything model 2: an application to 2D and 3D medical images

Patil, D.D., Deore, S.G.: Medical image segmentation: a review. International Journal of Computer Science and Mobile Computing 2

2013

Earlier work this paper cites.

Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G.: Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quantitative imaging in medicine and surgery 4

2014

Earlier work this paper cites.

Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34

2014

Earlier work this paper cites.

Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Computerized medical imaging and graphics 43

2015

Earlier work this paper cites.

Lemaître, G., Martí, R., Freixenet, J., Vilanova, J.C., Walker, P.M., Meriaudeau, F.: Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review. Computers in biology and medicine 60

2015

Earlier work this paper cites.

Pont-Tuset, J., Perazzi, F., Caelles, S., Arbeláez, P., Sorkine-Hornung, A., Van Gool, L.: The 2017 davis challenge on video object segmentation. arXiv preprint arXiv:1704.00675 (2017)

Original

2017

Earlier work this paper cites.

Prados, F., Ashburner, J., Blaiotta, C., Brosch, T., Carballido-Gamio, J., Cardoso, M.J., Conrad, B.N., Datta, E., Dávid, G., De Leener, B., et al.: Spinal cord grey matter segmentation challenge. Neuroimage 152

2017

Earlier work this paper cites.

Saha, A., Harowicz, M.R., Grimm, L.J., Kim, C.E., Ghate, S.V., Walsh, R., Mazurowski, M.A.: A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 dce-mri features. British journal of cancer 119

2018

Earlier work this paper cites.

Yin, S., Bi, J.: Medical image annotation based on deep transfer learning. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). pp. 47–49. IEEE (2018)

2018

Earlier work this paper cites.

Leclerc, S., Smistad, E., Pedrosa, J., Østvik, A., Cervenansky, F., Espinosa, F., Espeland, T., Berg, E.A.R., Jodoin, P.M., Grenier, T., et al.: Deep learning for segmentation using an open large-scale dataset in 2d echocardiography. IEEE transactions on medical imaging 38

2019

Earlier work this paper cites.

Rister, B., Shivakumar, K., Nobashi, T., Rubin, D.L.: Ct-org: Ct volumes with multiple organ segmentations [dataset]. The Cancer Imaging Archive 21

2019

Earlier work this paper cites.

Simpson, A.L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., Van Ginneken, B., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)

Original

2019

Earlier work this paper cites.

Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data in brief 28

2020

Earlier work this paper cites.

Gut, D.: X-ray images of the hip joints. Mendeley Data 1

2021

Earlier work this paper cites.

Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18

2021

Cited alongside, same era.

Marzola, F., Van Alfen, N., Doorduin, J., Meiburger, K.M.: Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment. Computers in Biology and Medicine 135

2021

Cited alongside, same era.

Ramesh, K., Kumar, G.K., Swapna, K., Datta, D., Rajest, S.S.: A review of medical image segmentation algorithms. EAI Endorsed Transactions on Pervasive Health and Technology 7

2021

Cited alongside, same era.

Wang, W., Feiszli, M., Wang, H., Tran, D.: Unidentified video objects: A benchmark for dense, open-world segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 10776–10785 (2021)

2021

Cited alongside, same era.

Gong, S., Zhong, Y., Ma, W., Li, J., Wang, Z., Zhang, J., Heng, P.A., Dou, Q.: 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable medical image segmentation. arXiv preprint arXiv:2306.13465 (2023)

Original

2023

Later among the works it cites.

Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al.: Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4015–4026 (2023)

2023

Later among the works it cites.

Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y.: Segment anything model for medical image analysis: an experimental study. Medical Image Analysis 89

2023

Later among the works it cites.

Wang, H., Guo, S., Ye, J., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., Fu, B., Zhang, S., He, J., Qiao, Y.: Sam-med3d (2023), https://arxiv.org/abs/2310.15161

Original

2023

Later among the works it cites.