The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge

Bakas, 2018, Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge Bengio, 2012, Deep learning of representations for unsupervised and transfer learning, 17 Bilic, 2019, The liver tumor segmentation benchmark (lits) Blake, 2019, Automatic renal nephrometry scoring using machine learning, European Urology Supplements, 18, e904, 10.1016/S1569-9056(19)30660-8 Brett, 2018, Nipy/nibabel: 2.3. 0, June, 1287921 Campbell, 2017, Renal mass and localized renal cancer: Aua guideline, J. Urol., 198, 520, 10.1016/j.juro.2017.04.100 Capitanio, 2016, Renal cancer, The Lancet, 387, 894, 10.1016/S0140-6736(15)00046-X Chawla, 2006, The natural history of observed enhancing renal masses: meta-analysis and review of the world literature, J. Urol., 175, 425, 10.1016/S0022-5347(05)00148-5 Çiçek, 2016, 3d u-net: learning dense volumetric segmentation from sparse annotation, 424 Deng, 2009, Imagenet: A large-scale hierarchical image database, 248 Efron, 1994 Farjam, 2007, An image analysis approach for automatic malignancy determination of prostate pathological images, Cytometry Part B: Clinical Cytometry: The Journal of the International Society for Analytical Cytology, 72, 227, 10.1002/cyto.b.20162 Ficarra, 2009, Preoperative aspects and dimensions used for an anatomical (padua) classification of renal tumours in patients who are candidates for nephron-sparing surgery, Eur. Urol., 56, 786, 10.1016/j.eururo.2009.07.040 Hayn, 2011, Renal nephrometry score predicts surgical outcomes of laparoscopic partial nephrectomy, BJU Int., 108, 876, 10.1111/j.1464-410X.2010.09940.x He, 2016, Deep residual learning for image recognition, 770 He, 2016, Identity mappings in deep residual networks, 630 He, 2019, Multi-task learning for the segmentation of thoracic organs at risk in ct images. Heimann, 2009, Comparison and evaluation of methods for liver segmentation from ct datasets, IEEE Trans Med Imaging, 28, 1251, 10.1109/TMI.2009.2013851 Heller, 2019, The role of publicly available data in miccai papers from 2014 to 2018 Heller, 2019, The kits19 challenge data: 300 kidney tumor cases with clinical context, ct semantic segmentations, and surgical outcomes Heller, 2017, A web-based platform for distributed annotation of computerized tomography scans, 136 Hollingsworth, 2006, Rising incidence of small renal masses: a need to reassess treatment effect, J. Natl. Cancer Inst., 98, 1331, 10.1093/jnci/djj362 Hou, 2019, Cascaded semantic segmentation for kidney and tumor Isensee, 2019, Automated design of deep learning methods for biomedical image segmentation Isensee, 2019, An attempt at beating the 3d u-net Isensee, 2018, nnU-Net: self-adapting framework for u-net-based medical image segmentation Isensee, 2019, nnU-Net: breaking the spell on successful medical image segmentation Kim, 2019, Association of prevalence of benign pathologic findings after partial nephrectomy with preoperative imaging patterns in the united states from 2007 to 2014, JAMA Surg, 154, 225, 10.1001/jamasurg.2018.4602 Kingma, 2014, Adam: a method for stochastic optimization Kutikov, 2011, Anatomic features of enhancing renal masses predict malignant and high-grade pathology: a preoperative nomogram using the renal nephrometry score, Eur. Urol., 60, 241, 10.1016/j.eururo.2011.03.029 Kutikov, 2009, The renal nephrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth, J. Urol., 182, 844, 10.1016/j.juro.2009.05.035 Larobina, 2014, Medical image file formats, J Digit Imaging, 27, 200, 10.1007/s10278-013-9657-9 Li, 2018, H-Denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes, IEEE Trans Med Imaging, 37, 2663, 10.1109/TMI.2018.2845918 Lin, 2014, Microsoft coco: Common objects in context, 740 Litjens, 2017, A survey on deep learning in medical image analysis, Med Image Anal, 42, 60, 10.1016/j.media.2017.07.005 Ma, 2019, Solution to the kidney tumor segmentation challenge 2019 Maier, 2017, Isles 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral mri, Med Image Anal, 35, 250, 10.1016/j.media.2016.07.009 Maier-Hein, 2018, Why rankings of biomedical image analysis competitions should be interpreted with care, Nat Commun, 9, 5217, 10.1038/s41467-018-07619-7 Mason, 2011, Su-e-t-33: pydicom: an open source dicom library, Med Phys, 38, 3493, 10.1118/1.3611983 McIntosh, 2018, Active surveillance for localized renal masses: tumor growth, delayed intervention rates, and> 5-yr clinical outcomes, Eur. Urol., 74, 157, 10.1016/j.eururo.2018.03.011 Millet, 2011, Characterization of small solid renal lesions: can benign and malignant tumors be differentiated with ct?, American journal of roentgenology, 197, 887, 10.2214/AJR.10.6276 Milletari, 2016, V-net: Fully convolutional neural networks for volumetric medical image segmentation, 565 Mir, 2017, Partial nephrectomy versus radical nephrectomy for clinical t1b and t2 renal tumors: a systematic review and meta-analysis of comparative studies, Eur. Urol., 71, 606, 10.1016/j.eururo.2016.08.060 Mu, 2019, Segmentation of kidney tumor by multi-resolution vb-nets Okhunov, 2011, The comparison of three renal tumor scoring systems: C-index, padua, and renal nephrometry scores, Journal of endourology, 25, 1921, 10.1089/end.2011.0301 Oktay, 2018, Attention U-Net: learning where to look for the pancreas Park, 2018, Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction, Radiology, 286, 800, 10.1148/radiol.2017171920 Paszke, 2017, Pytorch: tensors and dynamic neural networks in python with strong GPU acceleration, PyTorch: Tensors and dynamic neural networks in Python with strong GPU acceleration, 6 Patel, 2016, A prospective, comparative study of quality of life among patients with small renal masses choosing active surveillance and primary intervention, J. Urol., 196, 1356, 10.1016/j.juro.2016.04.073 R Core Team, 2020 Reinke, 2018, How to exploit weaknesses in biomedical challenge design and organization, 388 Richard, 2016, Active surveillance for renal neoplasms with oncocytic features is safe, J. Urol., 195, 581, 10.1016/j.juro.2015.09.067 Robson, 1963, Radical nephrectomy for renal cell carcinoma, J. Urol., 89, 37, 10.1016/S0022-5347(17)64494-X Ronneberger, 2015, U-net: Convolutional networks for biomedical image segmentation, 234 Scosyrev, 2014, Renal function after nephron-sparing surgery versus radical nephrectomy: results from eortc randomized trial 30904, Eur. Urol., 65, 372, 10.1016/j.eururo.2013.06.044 Shen, 2017, Deep learning in medical image analysis, Annu Rev Biomed Eng, 19, 221, 10.1146/annurev-bioeng-071516-044442 Simmons, 2010, Kidney tumor location measurement using the c index method, J. Urol., 183, 1708, 10.1016/j.juro.2010.01.005 Simmons, 2012, Diameter-axial-polar nephrometry: integration and optimization of renal and centrality index scoring systems, J. Urol., 188, 384, 10.1016/j.juro.2012.03.123 Spaliviero, 2015, Interobserver variability of renal, padua, and centrality index nephrometry score systems, World J Urol, 33, 853, 10.1007/s00345-014-1376-4 Taha, 2018, Kid-net: convolution networks for kidney vessels segmentation from ct-volumes, 463 Tang, 2018, Semi-automatic recist labeling on ct scans with cascaded convolutional neural networks, 405 Uzosike, 2018, Growth kinetics of small renal masses on active surveillance: variability and results from the dissrm registry, J. Urol., 199, 641, 10.1016/j.juro.2017.09.087 West, 1997, Comparison and evaluation of retrospective intermodality brain image registration techniques, J Comput Assist Tomogr, 21, 554, 10.1097/00004728-199707000-00007 Wiesenfarth, 2019, Methods and open-source toolkit for analyzing and visualizing challenge results Wolff, 2019, Probast: a tool to assess the risk of bias and applicability of prediction model studies, Ann. Intern. Med., 170, 51, 10.7326/M18-1376 Yushkevich, 2016, Itk-snap: An interactive tool for semi-automatic segmentation of multi-modality biomedical images, 3342 Zhang, 2016, Understanding deep learning requires rethinking generalization Zhang, 2019, Cascaded volumetric convolutional network for kidney tumor segmentation from ct volumes Zhuang, 2019, Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge