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Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years.
The liver tumor segmentation benchmark (lits)
Bilic, P., Christ, P.F., Vorontsov, E., Chlebus, G., Chen, H., Dou, Q., Fu, C.W., Han, X., Heng, P.A., Hesser, J., et al., 2019 · 1901
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Nandamuri, S., China, D., Mitra, P., Sheet, D., 2019 · 1901
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., 2019 · 1902
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Multi-domain adversarial learning
Schoenauer-Sebag, A., Heinrich, L., Schoenauer, M., Sebag, M., Wu, L.F., Altschuler, S.J., 2019 · 1903
Earlier work this paper cites.
Quantification and longitudinal trends of kidney, renal cyst, and renal parenchyma volumes in autosomal dominant polycystic kidney disease
King, B.F., Reed, J.E., Bergstralh, E.J., Sheedy, P.F., Torres, V.E., 2000 · 2000
Earlier work this paper cites.
Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks
Conze, P.H., Kavur, A.E., Gall, E.C.L., Gezer, N.S., Meur, Y.L., Selver, M.A., Rousseau, F., 2020b · 2001
Earlier work this paper cites.
Radiology of the spleen
Robertson, F., Leander, P., Ekberg, O., 2001 · 2001
Earlier work this paper cites.
An Overview of Current Evaluation Methods Used in Medical Image Segmentation
Yeghiazaryan, V., Voiculescu, I., 2015 · 2001
Earlier work this paper cites.
Spleen size: how well do linear ultrasound measurements correlate with three-dimensional ct volume assessments?
Lamb, P.M., Lund, A., Kanagasabay, R.R., Martin, A., Webb, J.A.W., Reznek, R.H., 2002 · 2002
Earlier work this paper cites.
Overfitting in making comparisons between variable selection methods
Reunanen, J., 2003 · 2003
Earlier work this paper cites.
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B., 2004 · 2004
Earlier work this paper cites.
Imaging evaluation of potential donors in living-donor liver transplantation
Low, G., Wiebe, E., Walji, A.H., Bigam, D.L., 2008 · 2007
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3d segmentation in the clinic: A grand challenge
Van Ginneken, B., Heimann, T., Styner, M., 2007 · 2007
Earlier work this paper cites.
3d segmentation in the clinic: a grand challenge ii-liver tumor segmentation, in: MICCAI workshop
Deng, X., Du, G., 2008 · 2008
Earlier work this paper cites.
Mri artifact reduction and quality improvement in the upper abdomen with propeller and prospective acquisition correction (pace) technique
Hirokawa, Y., Isoda, H., Maetani, Y.S., Arizono, S., Shimada, K., Togashi, K., 2008 · 2008
Earlier work this paper cites.
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
Earlier work this paper cites.
Integrating segmentation methods from different tools into a visualization program using an object-based plug-in interface
Fischer, F., Alper Selver, M., Hillen, W., Guzelis, C., 2010 · 2010
Earlier work this paper cites.
Pitfalls of supervised feature selection
Smialowski, P., Frishman, D., Kramer, S., 2010 · 2010
Earlier work this paper cites.
The “peeking” effect in supervised feature selection on diffusion tensor imaging data
Diciotti, S., Ciulli, S., Mascalchi, M., Giannelli, M., Toschi, N., 2013 · 2013
Earlier work this paper cites.
The virtual skeleton database: an open access repository for biomedical research and collaboration
Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P., 2013 · 2013
Earlier work this paper cites.
Who’s #1? : the science of rating and ranking. Princeton University Press
Langville, A.N., Meyer, C.D.C.D., 2013 · 2013
Earlier work this paper cites.
Assessing splenomegaly: Automated volumetric analysis of the spleen
Linguraru, M.G., Sandberg, J.K., Jones, E.C., Summers, R.M., 2013 · 2013
Earlier work this paper cites.
3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support. Springer New York, New York, NY
Kikinis, R., Pieper, S.D., Vosburgh, K.G., 2014 · 2014
Earlier work this paper cites.
Combining Pattern Classifiers: Methods and Algorithms: Second Edition. Wiley-Interscience. volume 9781118315
Kuncheva, L.I., 2014 · 2014
Cited alongside, same era.
Batch normalization: accelerating deep network training by reducing internal covariate shift, in: Proceedings of the 32nd International Conference on International Conference on Machine Learning-Volume 37, JMLR. org. pp. 448–456
Ioffe, S., Szegedy, C., 2015 · 2015
Cited alongside, same era.
Assessing splenic enlargement on ct by unidimensional measurement changes in patients with colorectal liver metastases
Joiner, B.J., Simpson, A.L., Leal, J.N., D’Angelica, M.I., Do, R.K.G., 2015 · 2015
Cited alongside, same era.
MICCAI multi-atlas labeling beyond the cranial vault – workshop and challenge
Landman, B., Xu, Z., Igelsias, J.E., Styner, M., Langerak, T.R., Klein, A., 2015 · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Multi-modal learning from unpaired images: Application to multi-organ segmentation in ct and mri, in: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 547–556
Valindria, V.V., Pawlowski, N., Rajchl, M., Lavdas, I., Aboagye, E.O., Rockall, A.G., Rueckert, D., Glocker, B., 2018 · 2018
Later among the works it cites.
Deep attentional features for prostate segmentation in ultrasound, in: MICCAI
Wang, Y., Deng, Z., Hu, X., Zhu, L., Yang, X., Xu, X., Heng, P.A., Ni, D., 2018 · 2018
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Group normalization, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19
Wu, Y., He, K., 2018 · 2018
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A novel focal tversky loss function with improved attention u-net for lesion segmentation, in: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), IEEE. pp. 683–687
Abraham, N., Khan, N.M., 2019 · 2019
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Computational anatomy for multi-organ analysis in medical imaging: A review
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Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Cited alongside, same era.
20th anniversary of the medical image analysis journal (MedIA)
Ayache, N., Duncan, J., 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Cited alongside, same era.
Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B., 2017 · 2016
Cited alongside, same era.
Challenges and benchmarks in bioimage analysis, in: Focus on Bio-Image Informatics. Springer, pp. 231–262
Kozubek, M., 2016 · 2016
Cited alongside, same era.
Modout: Learning to fuse modalities via stochastic regularization
Li, F., Neverova, N., Wolf, C., Taylor, G., 2016 · 2016
Cited alongside, same era.
Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: Visceral anatomy benchmarks
Jimenez-del Toro, O., Müller, H., Krenn, M., Gruenberg, K., Taha, A.A., Winterstein, M., Eggel, I., Foncubierta-Rodríguez, A., Goksel, O., Jakab, A., et al., 2016 · 2016
Cited alongside, same era.
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data
Guinney, J., Wang, T., Laajala, T.D., Winner, K.K., Bare, J.C., Neto, E.C., Khan, S.A., Peddinti, G., Airola, A., Pahikkala, T., et al., 2017 · 2017
Cited alongside, same era.
Cerrolaza, J.J., Picazo, M.L., Humbert, L., Sato, Y., Rueckert, D., Ángel González Ballester, M., Linguraru, M.G., 2019 · 2019
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Dual attention network for scene segmentation, in: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3141–3149
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H., 2019 · 2019
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A novel domain adaptation framework for medical image segmentation, in: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer International Publishing, Cham. pp. 289–298
Gholami, A., Subramanian, S., Shenoy, V., Himthani, N., Yue, X., Zhao, S., Jin, P., Biros, G., Keutzer, K., 2019 · 2019
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Grand challenges in biomedical image analysis
van Ginneken, B., Kerkstra, S., 2015 · 2019
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Cerebellum parcellation with convolutional neural networks, in: Medical Imaging 2019: Image Processing, International Society for Optics and Photonics. p. 109490K
Han, S., He, Y., Carass, A., Ying, S.H., Prince, J.L., 2019 · 2019
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nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation. Springer Vieweg, Wiesbaden
Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P.F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., Wirkert, S., Maier-Hein, K.H., 2019 · 2019
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Comparison of semi-automatic and deep learning based automatic methods for liver segmentation in living liver transplant donors
Kavur, A.E., Gezer, N.S., Barış, M., Şahin, Y., Özkan, S., Baydar, B., Yüksel, U., Kılıkçıer, C., Olut, S., Bozdağı Akar, G., Ünal, G., Dicle, O., Selver, M.A., 2020 · 2019
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CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data
Kavur, A.E., Selver, M.A., Dicle, O., Barış, M., Gezer, N.S., 2019 · 2019
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Segmentation of thoracic organs using pixel shuffle, in: Proceedings of the 2019 Challenge on Segmentation of THoracic Organs at Risk in CT Images, SegTHOR@ISBI 2019, April 8, 2019
Lachinov, D., 2019 · 2019
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Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions
Prevedello, L.M., Halabi, S.S., Shih, G., Wu, C.C., Kohli, M.D., Chokshi, F.H., Erickson, B.J., Kalpathy-Cramer, J., Andriole, K.P., Flanders, A.E., 2019 · 2019
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Attention gated networks: Learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., Rueckert, D., 2019 · 2019
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URL: https://kits19.grand-challenge.org/ . accessed: 2019-07-08
Weight, C., Papanikolopoulos, N., Kalapara, A., Heller, N., 2019 · 2019
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Cascaded multi-scale convolutional encoder-decoders for breast mass segmentation in high-resolution mammograms, in: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany. pp. 6738–6741
Yan, Y., Conze, P.H., Decencière, E., Lamard, M., Quellec, G., Cochener, B., Coatrieux, G., 2019 · 2019
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Unsupervised domain adaptation via disentangled representations: Application to cross-modality liver segmentation, in: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.T., Khan, A. (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Springer International Publishing, Cham. pp. 255–263
Yang, J., Dvornek, N.C., Zhang, F., Chapiro, J., Lin, M., Duncan, J.S., 2019 · 2019
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Focusnetv2: Imbalanced large and small organ segmentation with adversarial shape constraint for head and neck ct images
Gao, Y., Huang, R., Yang, Y., Zhang, J., Shao, K., Tao, C., Chen, Y., Metaxas, D.N., Li, H., Chen, M., 2020 · 2020
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Abdominal multi-organ auto-segmentation using 3d-patch-based deep convolutional neural network
Kim, H., Jung, J., Kim, J., Cho, B., Kwak, J., Jang, J.Y., Lee, S.w., Lee, J.G., Yoon, S.M., 2020 · 2020
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Multi-scale self-guided attention for medical image segmentation
Sinha, A., Dolz, J., 2020 · 2020
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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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