Fetching the paper…
Reading the bibliography…
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
Machine learning with multi-site imaging data: An empirical study on the impact of scanner effects
Glocker, B., Robinson, R., Castro, D. C., Dou, Q., & Konukoglu, E. (2019) · 1910
Earlier work this paper cites.
Tumor burden analysis on computed tomography by automated liver and tumor segmentation
Linguraru, M. G., Richbourg, W. J., Liu, J., Watt, J. M., Pamulapati, V., Wang, S., & Summers, R. M. (2012) · 1976
Earlier work this paper cites.
Tumor burden assessment and its implication for a prognostic model in advanced diffuse large-cell lymphoma
Jagannath, S., Velasquez, W. S., Tucker, S. L., Fuller, L. M., McLaughlin, P. W., Manning, J. T., North, L. B., & Cabanillas, F. C. (1986) · 1986
Earlier work this paper cites.
Snakes: Active contour models
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · 1988
Earlier work this paper cites.
Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations
Osher, S., & Sethian, J. A. (1988) · 1988
Earlier work this paper cites.
Active shape models-their training and application
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995) · 1995
Earlier work this paper cites.
Surgical resection of colorectal carcinoma metastases to the liver: a prognostic scoring system to improve case selection, based on 1568 patients
Nordlinger, B., Guiguet, M., Vaillant, J.-C., Balladur, P., Boudjema, K., Bachellier, P., & Jaeck, D. (1996) · 1996
Earlier work this paper cites.
Diagnostic imaging approaches and relationship to hepatobiliary cancer staging and therapy
Hann, L. E., Winston, C. B., Brown, K. T., & Akhurst, T. (2000) · 2000
Earlier work this paper cites.
Fully automatic anatomical, pathological, and functional segmentation from ct scans for hepatic surgery
Soler, L., Delingette, H., Malandain, G., Montagnat, J., Ayache, N., Koehl, C., Dourthe, O., Malassagne, B., Smith, M., Mutter, D. et al. (2001) · 2001
Earlier work this paper cites.
Construction of an abdominal probabilistic atlas and its application in segmentation
Park, H., Bland, P. H., & Meyer, C. R. (2003) · 2003
Earlier work this paper cites.
The clinical value of tumor burden at diagnosis in hodgkin lymphoma
Gobbi, P. G., Broglia, C., Di Giulio, G., Mantelli, M., Anselmo, P., Merli, F., Zinzani, P. L., Rossi, G., Callea, V., Iannitto, E. et al. (2004) · 2004
Earlier work this paper cites.
Segmentation of the liver using a 3D statistical shape model
Lamecker, H., Lange, T., & Seebass, M. (2004) · 2004
Earlier work this paper cites.
Uncertainty evaluation metric for brain tumour segmentation
Mehta, R., Filos, A., Gal, Y., & Arbel, T. (2020) · 2005
Earlier work this paper cites.
Active shape models for a fully automated 3d segmentation of the liver–an evaluation on clinical data
Heimann, T., Wolf, I., & Meinzer, H.-P. (2006) · 2006
Earlier work this paper cites.
Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic ct scans
Kuhnigk, J.-M., Dicken, V., Bornemann, L., Bakai, A., Wormanns, D., Krass, S., & Peitgen, H.-O. (2006) · 2006
Earlier work this paper cites.
A machine learning approach for locating boundaries of liver tumors in ct images
Li, Y., Hara, S., & Shimura, K. (2006) · 2006
Earlier work this paper cites.
User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · 2006
Earlier work this paper cites.
Constructing a probabilistic model for automated liver region segmentation using non-contrast x-ray torso ct images
Zhou, X., Kitagawa, T., Hara, T., Fujita, H., Zhang, X., Yokoyama, R., Kondo, H., Kanematsu, M., & Hoshi, H. (2006) · 2006
Earlier work this paper cites.
Oncotreat: a software assistant for cancer therapy monitoring
Bornemann, L., Dicken, V., Kuhnigk, J.-M., Wormanns, D., Shin, H.-O., Bauknecht, H.-C., Diehl, V., Fabel, M., Meier, S., Kress, O. et al. (2007) · 2007
Earlier work this paper cites.
Automatic segmentation of single and multiple neoplastic hepatic lesions in ct images
Ciecholewski, M., & Ogiela, M. R. (2007) · 2007
Earlier work this paper cites.
Semi-automatic segmentation of the liver and its evaluation on the miccai 2007 grand challenge data set
Dawant, B. M., Li, R., Lennon, B., & Li, S. (2007) · 2007
Earlier work this paper cites.
A shape-guided deformable model with evolutionary algorithm initialization for 3d soft tissue segmentation
Heimann, T., Münzing, S., Meinzer, H.-P., & Wolf, I. (2007) · 2007
Earlier work this paper cites.
Follow-up ct measurement of liver malignoma according to recist and who vs. volumetry
Heussel, C. P., Meier, S., Wittelsberger, S., Götte, H., Mildenberger, P., & Kauczor, H.-U. (2007) · 2007
Earlier work this paper cites.
Shape constrained automatic segmentation of the liver based on a heuristic intensity model
Kainmüller, D., Lange, T., & Lamecker, H. (2007) · 2007
Earlier work this paper cites.
Fully automatic liver segmentation through graph-cut technique
Massoptier, L., & Casciaro, S. (2007) · 2007
Earlier work this paper cites.
Automatic segmentation of the liver in computed tomography scans with voxel classification and atlas matching
van Rikxoort, E., Arzhaeva, Y., & van Ginneken, B. (2007) · 2007
Earlier work this paper cites.
Global-to-local shape matching for liver segmentation in ct imaging
Saddi, K. A., Rousson, M., Chefd’hotel, C., & Cheriet, F. (2007) · 2007
Earlier work this paper cites.
Atlas based liver segmentation using nonrigid registration with a b-spline transformation model
Slagmolen, P., Elen, A., Seghers, D., Loeckx, D., Maes, F., & Haustermans, K. (2007) · 2007
Earlier work this paper cites.
Disentangling human error from the ground truth in segmentation of medical images
Zhang, L., Tanno, R., Xu, M.-C., Jacob, J., Ciccarelli, O., Barkhof, F., & Alexander, D. C. (2020b) · 2007
Earlier work this paper cites.
Liver tumor segmentation in ct images using probabilistic methods
Ben-Dan, I., & Shenhav, E. (2008) · 2008
Earlier work this paper cites.
3d segmentation in the clinic: a grand challenge ii-liver tumor segmentation
Deng, X., & Du, G. (2008) · 2008
Earlier work this paper cites.
Liver tumor segmentation using implicit surface evolution
Häme, Y. (2008) · 2008
Earlier work this paper cites.
Hierarchical, learning-based automatic liver segmentation
Ling, H., Zhou, S. K., Zheng, Y., Georgescu, B., Suehling, M., & Comaniciu, D. (2008) · 2008
Earlier work this paper cites.
A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from ct scans
Massoptier, L., & Casciaro, S. (2008) · 2008
Earlier work this paper cites.
Segmentation of liver metastases in ct scans by adaptive thresholding and morphological processing
Moltz, J. H., Bornemann, L., Dicken, V., & Peitgen, H. (2008) · 2008
Cited alongside, same era.
Contrast enhancement for liver tumor identification
Nugroho, H. A., Ihtatho, D., & Nugroho, H. (2008) · 2008
Cited alongside, same era.
Ensemble segmentation using adaboost with application to liver lesion extraction from a ct volume
Shimizu, A., Narihira, T., Furukawa, D., Kobatake, H., Nawano, S., & Shinozaki, K. (2008) · 2008
Cited alongside, same era.
Segmentation of liver metastases using a level set method with spiral-scanning technique and supervised fuzzy pixel classification
Smeets, D., Stijnen, B., Loeckx, D., De Dobbelaer, B., & Suetens, P. (2008) · 2008
Cited alongside, same era.
Radiotherapy plus chemotherapy with or without surgical resection for stage iii non-small-cell lung cancer: a phase iii randomised controlled trial
Albain, K. S., Swann, R. S., Rusch, V. W., Turrisi, A. T., Shepherd, F. A., Smith, C., Chen, Y., Livingston, R. B., Feins, R. H., Gandara, D. R. et al. (2009) · 2009
Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography
Moghbel, M., Mashohor, S., Mahmud, R., & Saripan, M. I. B. (2017) · 2017
Later among the works it cites.
Automatic liver tumor segmentation in ct with fully convolutional neural networks and object-based postprocessing
Chlebus, G., Schenk, A., Moltz, J. H., van Ginneken, B., Hahn, H. K., & Meine, H. (2018) · 2018
Later among the works it cites.
Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation
Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2018) · 2018
Later among the works it cites.
Percutaneous ablation for hepatocellular carcinoma: comparison of various ablation techniques and surgery
Shiina, S., Sato, K., Tateishi, R., Shimizu, M., Ohama, H., Hatanaka, T., Takawa, M., Nagamatsu, H., & Imai, Y. (2018) · 2018
Later among the works it cites.
Midas - original datasets
Cleary, K. (2017) · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
New response evaluation criteria in solid tumours: revised recist guideline (version 1.1)
Eisenhauer, E., Therasse, P., Bogaerts, J., Schwartz, L., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M. et al. (2009) · 2009
Cited alongside, same era.
Comparison and evaluation of methods for liver segmentation from ct datasets
Heimann, T., Van Ginneken, B., Styner, M., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., Bello, F., Binnig, G., Bischof, H., Bornik, A., Cashman, P., Chi, Y., Córdova, A., Dawant, B., Fidrich, M., Furst, J., Furukawa, D., Grenacher, L., Hornegger, J., Kainmüller, D., Kitney, R., Kobatake, H., Lamecker, H., Lange, T., Lee, J., Lennon, B., Li, R., Li, S., Meinzer, H., Németh, G., Raicu, D., Rau, A., Van Rikxoort, E., Rousson, M., Ruskó, L., Saddi, K., Schmidt, G., Seghers, D., Shimizu, A., Slagmolen, P., Sorantin, E., Soza, G., Susomboon, R., Waite, J., Wimmer, A., & Wolf, I. (2009) · 2009
Cited alongside, same era.
Advanced segmentation techniques for lung nodules, liver metastases, and enlarged lymph nodes in ct scans
Moltz, J. H., Bornemann, L., Kuhnigk, J.-M., Dicken, V., Peitgen, E., Meier, S., Bolte, H., Fabel, M., Bauknecht, H.-C., Hittinger, M. et al. (2009) · 2009
Cited alongside, same era.
Comparison of adaboost and logistic regression for detecting colorectal cancer patients with synchronous liver metastasis
Wen, J., Zhang, X., Xu, Y., Li, Z., & Liu, L. (2009) · 2009
Cited alongside, same era.
Fully automatic liver tumor segmentation from abdominal ct scans
Abdel-massieh, N. H., Hadhoud, M. M., & Amin, K. M. (2010) · 2010
Cited alongside, same era.
Intra-and interobserver variability of linear and volumetric measurements of brain metastases using contrast-enhanced magnetic resonance imaging
Bauknecht, H.-C., Romano, V. C., Rogalla, P., Klingebiel, R., Wolf, C., Bornemann, L., Hamm, B., & Hein, P. A. (2010) · 2010
Cited alongside, same era.
Mann-whitney u test
McKnight, P. E., & Najab, J. (2010) · 2010
Cited alongside, same era.
Clinical outcomes of primary arterial embolization in severe hepatic trauma: A systematic review
Virdis, F., Reccia, I., Di Saverio, S., Tugnoli, G., Kwan, S., Kumar, J., Atzeni, J., & Podda, M. (2019) · 2019
Closest in time.
Volumetric attention for 3d medical image segmentation and detection
Wang, X., Han, S., Chen, Y., Gao, D., & Vasconcelos, N. (2019) · 2019
Closest in time.
Biomarker localization from deep learning regression networks
Cano-Espinosa, C., González, G., Washko, G. R., Cazorla, M., & Estépar, R. S. J. (2020) · 2020
Closest in time.
Causality matters in medical imaging
Castro, D. C., Walker, I., & Glocker, B. (2020) · 2020
Closest in time.
Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction
Fang, X., & Yan, P. (2020) · 2020
Closest in time.
Learning semantics-enriched representation via self-discovery, self-classification, and self-restoration
Haghighi, F., Taher, M. R. H., Zhou, Z., Gotway, M. B., & Liang, J. (2020) · 2020
Closest in time.
Multi-organ segmentation via co-training weight-averaged models from few-organ datasets
Huang, R., Zheng, Y., Hu, Z., Zhang, S., & Li, H. (2020) · 2020
Closest in time.
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2020) · 2020
Closest in time.
Cutting-edge 3d medical image segmentation methods in 2020: Are happy families all alike?
Ma, J. (2021) · 2020
Closest in time.
Learning geodesic active contours for embedding object global information in segmentation cnns
Ma, J., He, J., & Yang, X. (2020) · 2020
Closest in time.
Bias: Transparent reporting of biomedical image analysis challenges
Maier-Hein, L., Reinke, A., Kozubek, M., Martel, A. L., Arbel, T., Eisenmann, M., Hanbury, A., Jannin, P., Müller, H., Onogur, S. et al. (2020) · 2020
Closest in time.
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K. et al. (2020) · 2020
Closest in time.
Deep learning-enabled multi-organ segmentation in whole-body mouse scans
Schoppe, O., Pan, C., Coronel, J., Mai, H., Rong, Z., Todorov, M. I., Müskes, A., Navarro, F., Li, H., Ertürk, A. et al. (2020) · 2020
Closest in time.
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R. et al. (2020) · 2020
Closest in time.
Universal loss reweighting to balance lesion size inequality in 3d medical image segmentation
Shirokikh, B., Shevtsov, A., Kurmukov, A., Dalechina, A., Krivov, E., Kostjuchenko, V., Golanov, A., & Belyaev, M. (2020) · 2020
Closest in time.
Eˆnet: An edge enhanced network for accurate liver and tumor segmentation on ct scans
Tang, Y., Tang, Y., Zhu, Y., Xiao, J., & Summers, R. M. (2020) · 2020
Closest in time.
Machine learning analysis of whole mouse brain vasculature
Todorov, M., Paetzold, J. C., Schoppe, O., Tetteh, G., Shit, S., Efremov, V., Todorov-Völgyi, K., Düring, M., Dichgans, M., Piraud, M. et al. (2020) · 2020
Closest in time.
Conquering data variations in resolution: A slice-aware multi-branch decoder network
Wang, S., Cao, S., Chai, Z., Wei, D., Ma, K., Wang, L., & Zheng, Y. (2020) · 2020
Closest in time.
Learning from multiple datasets with heterogeneous and partial labels for universal lesion detection in ct
Yan, K., Cai, J., Zheng, Y., Harrison, A. P., Jin, D., Tang, Y.-b., Tang, Y.-X., Huang, L., Xiao, J., & Lu, L. (2020) · 2020
Closest in time.
Learning shape reconstruction from sparse measurements with neural implicit functions
Amiranashvili, T., Lüdke, D., Li, H., Zachow, S. et al. (2021) · 2021
Closest in time.
Transferable visual words: Exploiting the semantics of anatomical patterns for self-supervised learning
Haghighi, F., Taher, M. R. H., Zhou, Z., Gotway, M. B., & Liang, J. (2021) · 2021
Closest in time.
Chaos challenge-combined (ct-mr) healthy abdominal organ segmentation
Kavur, A. E., Gezer, N. S., Barış, M., Aslan, S., Conze, P.-H., Groza, V., Pham, D. D., Chatterjee, S., Ernst, P., Özkan, S. et al. (2021) · 2021
Closest in time.
The federated tumor segmentation (fets) challenge
Pati, S., Baid, U., Zenk, M., Edwards, B., Sheller, M., Reina, G. A., Foley, P., Gruzdev, A., Martin, J., Albarqouni, S. et al. (2021) · 2021
Closest in time.
Pairwise learning for medical image segmentation
Wang, R., Cao, S., Ma, K., Zheng, Y., & Meng, D. (2021) · 2021
Closest in time.
Methods and open-source toolkit for analyzing and visualizing challenge results
Wiesenfarth, M., Reinke, A., Landman, B. A., Eisenmann, M., Saiz, L. A., Cardoso, M. J., Maier-Hein, L., & Kopp-Schneider, A. (2021) · 2021
Closest in time.
Context-aware polyunet for liver and lesion segmentation from abdominal ct images
Zhang, L., & Yu, S. C.-H. (2021) · 2021
Closest in time.
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
Closest in time.
What makes for automatic reconstruction of pulmonary segments
Kuang, K., Zhang, L., Li, J., Li, H., Chen, J., Du, B., & Yang, J. (2022) · 2022
Closest in time.
Implicitatlas: Learning deformable shape templates in medical imaging
Yang, J., Wickramasinghe, U., Ni, B., & Fua, P. (2022) · 2022
Closest in time.