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The Segment Anything Model (SAM) is the first foundation model for general image segmentation.
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Heller, N., Sathianathen, N., Kalapara, A., Walczak, E., Moore, K., Kaluzniak, H., Rosenberg, J., Blake, P., Rengel, Z., Oestreich, M., Dean, J., Tradewell, M., Shah, A., Tejpaul, R., Edgerton, Z., Peterson, M., Raza, S., Regmi, S., Papanikolopoulos, N., Weight, C., 2020 · 1904
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Jaccard, P., 1908 · 1908
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Measures of the amount of ecologic association between species
Dice, L.R., 1945 · 1945
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Elliptic fourier features of a closed contour
Kuhl, F.P., Giardina, C.R., 1982 · 1982
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Learning for structured prediction using approximate subgradient descent with working sets, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1987–1994
Lucchi, A., Li, Y., Fua, P., 2013 · 1994
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Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
Hoover, A., Kouznetsova, V., Goldbaum, M., 2000 · 2000
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Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels
Hoover, A., Goldbaum, M., 2003 · 2003
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3d slicer, in: 2004 2nd IEEE international symposium on biomedical imaging: nano to macro (IEEE Cat No. 04EX821), IEEE. pp. 632–635
Pieper, S., Halle, M., Kikinis, R., 2004 · 2004
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Generalized overlap measures for evaluation and validation in medical image analysis
Crum, W.R., Camara, O., Hill, D.L., 2006 · 2006
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Comparison and evaluation of methods for liver segmentation from ct datasets
Heimann, T., Van Ginneken, B., Styner, M.A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al., 2009 · 2009
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An integrated micro-and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy
Cardona, A., Saalfeld, S., Preibisch, S., Schmid, B., Cheng, A., Pulokas, J., Tomancak, P., Hartenstein, V., 2010 · 2010
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Learning local shape and appearance for segmentation of knee cartilage in 3d mri, in: Medical Image Analysis for the Clinic: a Grand Challenge. In Proceedings of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2010), Beijing, China, pp. 231–240
Lee, S., Shim, H., Park, S.H., Yun, I.D., Lee, S.U., 2010 · 2010
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Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes
Shao, P., Qin, C., Yin, C., Meng, X., Ju, X., Li, J., Lv, Q., Zhang, W., Xu, Z., 2011 · 2011
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Precise segmental renal artery clamping under the guidance of dual-source computed tomography angiography during laparoscopic partial nephrectomy
Shao, P., Tang, L., Li, P., Xu, Y., Qin, C., Cao, Q., Ju, X., Meng, X., Lv, Q., Li, J., et al., 2012 · 2012
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Automated segmentation of prostate mr images using prior knowledge enhanced random walker, in: 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), IEEE. pp. 1–7
Li, A., Li, C., Wang, X., Eberl, S., Feng, D.D., Fulham, M., 2013 · 2013
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Two public chest x-ray datasets for computer-aided screening of pulmonary diseases
Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G., 2014 · 2014
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Evaluation of prostate segmentation algorithms for mri: the promise12 challenge
Litjens, G., Toth, R., Van De Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al., 2014 · 2014
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Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., Vilariño, F., 2015 · 2015
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Comparative validation of polyp detection methods in video colonoscopy: results from the miccai 2015 endoscopic vision challenge
Bernal, J., Tajkbaksh, N., Sanchez, F.J., Matuszewski, B.J., Chen, H., Yu, L., Angermann, Q., Romain, O., Rustad, B., Balasingham, I., et al., 2017 · 2015
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Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge, in: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p. 12
Landman, B., Xu, Z., Igelsias, J., Styner, M., Langerak, T., Klein, A., 2015 · 2015
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Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review
Lemaître, G., Martí, R., Freixenet, J., Vilanova, J.C., Walker, P.M., Meriaudeau, F., 2015 · 2015
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Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late gadolinium enhancement mr images
Karim, R., Bhagirath, P., Claus, P., Housden, R.J., Chen, Z., Karimaghaloo, Z., Sohn, H.M., Rodríguez, L.L., Vera, S., Albà, X., et al., 2016 · 2016
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Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores
Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J.P., Duits, R., ter Haar Romeny, B.M., 2016 · 2016
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Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C., 2017 · 2017
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Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge
Setio, A.A.A., Traverso, A., De Bel, T., Berens, M.S., Van Den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M.E., Geurts, B., et al., 2017 · 2017
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Gland segmentation in colon histology images: The glas challenge contest
Sirinukunwattana, K., Pluim, J.P., Chen, H., Qi, X., Heng, P.A., Guo, Y.B., Wang, L.Y., Matuszewski, B.J., Bruni, E., Sanchez, U., et al., 2017 · 2017
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Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al., 2018 · 2018
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Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?
Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al., 2018 · 2018
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Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic), in: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), IEEE. pp. 168–172
Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al., 2018 · 2018
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Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research
Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., Meriaudeau, F., 2018 · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C., Kittler, H., 2018 · 2018
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Neural segmentation of seeding rois (srois) for pre-surgical brain tractography
Avital, I., Nelkenbaum, I., Tsarfaty, G., Konen, E., Kiryati, N., Mayer, A., 2019 · 2019
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Mild-net: Minimal information loss dilated network for gland instance segmentation in colon histology images
Graham, S., Chen, H., Gamper, J., Dou, Q., Heng, P.A., Snead, D., Tsang, Y.W., Rajpoot, N., 2019 · 2019
Cited alongside, same era.
Palm: Pathologic myopia challenge
Huazhu, F., Fei, L., José, I., 2019 · 2019
Cited alongside, same era.
Deep learning for segmentation using an open large-scale dataset in 2d echocardiography
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., 2019 · 2019
Cited alongside, same era.
An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced t1-weighted and high-resolution t2-weighted mri
Shapey, J., Wang, G., Dorent, R., Dimitriadis, A., Li, W., Paddick, I., Kitchen, N., Bisdas, S., Saeed, S.R., Ourselin, S., et al., 2019 · 2019
Cited alongside, same era.
Multi-site infant brain segmentation algorithms: the iseg-2019 challenge
Sun, Y., Gao, K., Wu, Z., Li, G., Zong, X., Lei, Z., Wei, Y., Ma, J., Yang, X., Feng, X., et al., 2021 · 2019
A coarse-to-fine framework for the 2021 kidney and kidney tumor segmentation challenge, in: Kidney and Kidney Tumor Segmentation: MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Springer, pp. 53–58
Zhao, Z., Chen, H., Wang, L., 2022 · 2021
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The medical segmentation decathlon
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., Ronneberger, O., Summers, R.M., et al., 2022 · 2022
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Monai: An open-source framework for deep learning in healthcare
Cardoso, M.J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., Murrey, B., Myronenko, A., Zhao, C., et al., 2022 · 2022
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Focalclick: Towards practical interactive image segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1300–1309
Chen, X., Zhao, Z., Zhang, Y., Duan, M., Qi, D., Zhao, H., 2022 · 2022
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Cited alongside, same era.
SIIM-ACR Pneumothorax Segmentation
Zawacki, A., Wu, C., Shih, G., Elliott, J., Fomitchev, M., Hussain, M., ParasLakhani, Culliton, P., Bao, S., 2019 · 2019
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale, in: International Conference on Learning Representations
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al., 2020 · 2020
Cited alongside, same era.
Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation
He, Y., Yang, G., Yang, J., Chen, Y., Kong, Y., Wu, J., Tang, L., Zhu, X., Dillenseger, J.L., Shao, P., et al., 2020 · 2020
Cited alongside, same era.
Kvasir-seg: A segmented polyp dataset, in: International Conference on Multimedia Modeling, Springer. pp. 451–462
Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., de Lange, T., Johansen, D., Johansen, H.D., 2020 · 2020
Cited alongside, same era.
Reducing the hausdorff distance in medical image segmentation with convolutional neural networks
Karimi, D., Salcudean, S.E., 2020 · 2020
Cited alongside, same era.
Self-supervised feature learning via exploiting multi-modal data for retinal disease diagnosis
Li, X., Jia, M., Islam, M.T., Yu, L., Xing, L., 2020 · 2020
Cited alongside, same era.
A vertebral segmentation dataset with fracture grading
Löffler, M.T., Sekuboyina, A., Jacob, A., Grau, A.L., Scharr, A., El Husseini, M., Kallweit, M., Zimmer, C., Baum, T., Kirschke, J.S., 2020 · 2020
Cited alongside, same era.
Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R., 2022 · 2022
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Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Ji, Y., Bai, H., Yang, J., Luo, P., 2022 · 2022
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Sketch guided and progressive growing gan for realistic and editable ultrasound image synthesis
Liang, J., Yang, X., Huang, Y., Li, H., He, S., Hu, X., Chen, Z., Xue, W., Cheng, J., Ni, D., 2022 · 2022
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Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph segmentation
Liu, W., Yang, H., Tian, T., Cao, Z., Pan, X., Xu, W., Jin, Y., Gao, F., 2022 · 2022
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Word: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from ct image
Luo, X., Liao, W., Xiao, J., Chen, J., Song, T., Zhang, X., Li, K., Metaxas, D.N., Wang, G., Zhang, S., 2022 · 2022
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Hasa: hybrid architecture search with aggregation strategy for echinococcosis classification and ovary segmentation in ultrasound images
Qian, J., Li, R., Yang, X., Huang, Y., Luo, M., Lin, Z., Hong, W., Huang, R., Fan, H., Ni, D., et al., 2022 · 2022
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Highly accurate dichotomous image segmentation, in: European Conference on Computer Vision, Springer. pp. 38–56
Qin, X., Dai, H., Hu, X., Fan, D.P., Shao, L., Van Gool, L., 2022 · 2022
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Autolaparo: A new dataset of integrated multi-tasks for image-guided surgical automation in laparoscopic hysterectomy, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 486–496
Wang, Z., Lu, B., Long, Y., Zhong, F., Cheung, T.H., Dou, Q., Liu, Y., 2022 · 2022
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Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network
Yoon, D., Kong, H.J., Kim, B.S., Cho, W.S., Lee, J.C., Cho, M., Lim, M.H., Yang, S.Y., Lim, S.H., Lee, J., et al., 2022 · 2022
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The liver tumor segmentation benchmark (lits)
Bilic, P., Christ, P., Li, H.B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G.E.H., Chartrand, G., et al., 2023 · 2023
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Universeg: Universal medical image segmentation
Butoi*, V.I., Ortiz*, J.J.G., Ma, T., Sabuncu, M.R., Guttag, J., Dalca, A.V., 2023 · 2023
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Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation
Chen, C., Miao, J., Wu, D., Yan, Z., Kim, S., Hu, J., Zhong, A., Liu, Z., Sun, L., Li, X., Liu, T., Heng, P.A., Li, Q., 2023 · 2023
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Cheng, J., Ye, J., Deng, Z., Chen, J., Li, T., Wang, H., Su, Y., Huang, Z., Chen, J., Jiang, L., et al., 2023 · 2023
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Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging, in: Medical Imaging with Deep Learning, short paper track
Deng, R., Cui, C., Liu, Q., Yao, T., Remedios, L.W., Bao, S., Landman, B.A., Tang, Y., Wheless, L.E., Coburn, L.A., et al., 2023 · 2023
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Accuracy of segment-anything model (sam) in medical image segmentation tasks
He, S., Bao, R., Li, J., Grant, P.E., Ou, Y., 2023 · 2023
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Segment anything, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 4015–4026
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollar, P., Girshick, R., 2023 · 2023
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Mediar: Harmony of data-centric and model-centric for multi-modality microscopy, in: Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, PMLR. pp. 1–16
Lee, G., Kim, S., Kim, J., Yun, S.Y., 2023 · 2023
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Semantic-sam: Segment and recognize anything at any granularity
Li, F., Zhang, H., Sun, P., Zou, X., Liu, S., Yang, J., Li, C., Zhang, L., Gao, J., 2023 · 2023
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Segment anything in medical images
Ma, J., Wang, B., 2023 · 2023
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The multi-modality cell segmentation challenge: Towards universal solutions
Ma, J., Xie, R., Ayyadhury, S., Ge, C., Gupta, A., Gupta, R., Gu, S., Zhang, Y., Lee, G., Kim, J., Lou, W., Li, H., Upschulte, E., Dickscheid, T., de Almeida, J.G., Wang, Y., Han, L., Yang, X., Labagnara, M., Rahi, S.J., Kempster, C., Pollitt, A., Espinosa, L., Mignot, T., Middeke, J.M., Eckardt, J.N., Li, W., Li, Z., Cai, X., Bai, B., Greenwald, N.F., Valen, D.V., Weisbart, E., Cimini, B.A., Li, Z., Zuo, C., Brück, O., Bader, G.D., Wang, B., 2023 · 2023
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Exploring the zero-shot capabilities of the segment anything model (sam) in 2d medical imaging: A comprehensive evaluation and practical guideline
Mattjie, C., Vinicius de Moura, L., Cappelari Ravazio, R., Silveira Kupssinskü, L., Parraga, O., Mussi Delucis, M., Coelho Barros, R., 2023 · 2023
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Segment anything model for medical image analysis: an experimental study
Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y., 2023 · 2023
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Brain extraction comparing segment anything model (sam) and fsl brain extraction tool
Mohapatra, S., Gosai, A., Schlaug, G., 2023 · 2023
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Han-seg: The head and neck organ-at-risk ct & mr segmentation dataset
Podobnik, G., Strojan, P., Peterlin, P., Ibragimov, B., Vrtovec, T., 2023 · 2023
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Abdomenatlas-8k: Annotating 8,000 CT volumes for multi-organ segmentation in three weeks, in: Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track
Qu, C., Zhang, T., Qiao, H., Liu, J., Tang, Y., Yuille, A., Zhou, Z., 2023 · 2023
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Can sam segment anything? when sam meets camouflaged object detection
Tang, L., Xiao, H., Li, B., 2023 · 2023
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Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images
Wasserthal, J., Breit, H.C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D.T., Cyriac, J., Yang, S., Bach, M., Segeroth, M., 2023 · 2023
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Medical sam adapter: Adapting segment anything model for medical image segmentation
Wu, J., Fu, R., Fang, H., Liu, Y., Wang, Z., Xu, Y., Jin, Y., Arbel, T., 2023 · 2023
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Zhou, T., Zhang, Y., Zhou, Y., Wu, Y., Gong, C., 2023 · 2023
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The multimodal brain tumor image segmentation benchmark (brats)
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al., 2014 · 2024
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