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Size measurements of tumor manifestations on follow-up CT examinations are crucial for evaluating treatment outcomes in cancer patients.
"grabcut" interactive foreground extraction using iterated graph cuts
C. Rother, V. Kolmogorov, and A. Blake · 2004
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Volume rendering in the presence of partial volume effects
A. Souza, J. Udupa, and P. Saha · 2004
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New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
E. A. Eisenhauer, P. Therasse, J. Bogaerts, et al · 2009
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Comparison and evaluation of methods for liver segmentation from CT datasets
T. Heimann, B. Van Ginneken, M. A. Styner, et al · 2009
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The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans
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Workflow-centred evaluation of an automatic lesion tracking software for chemotherapy monitoring by CT
J. H. Moltz, M. D’Anastasi, A. Kießling, et al · 2012
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A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations
H. R. Roth, L. Lu, A. Seff, et al · 2014
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The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload
R. J. McDonald, K. M. Schwartz, L. J. Eckel, et al · 2015
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Radiomics: images are more than pictures, they are data
R. J. Gillies, P. E. Kinahan, and H. Hricak · 2016
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Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database
C. Jacobs, E. M. van Rikxoort, K. Murphy, et al · 2016
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RECIST 1.1—update and clarification: From the RECIST committee
L. H. Schwartz, S. Litière, E. De Vries, et al · 2016
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Observer variability in RECIST-based tumour burden measurements: a meta-analysis
S. H. Yoon, K. W. Kim, J. M. Goo, et al · 2016
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Accurate weakly-supervised deep lesion segmentation using large-scale clinical annotations: Slice-propagated 3D mask generation from 2D RECIST
J. Cai, Y. Tang, L. Lu, et al · 2018
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Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database
K. Yan, X. Wang, L. Lu, et al · 2018
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nnU-Net: Breaking the spell on successful medical image segmentation
F. Isensee, J. Petersen, S. A. Kohl, et al · 2019
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Crowds cure cancer: Crowdsourced data collected at the RSNA 2018 annual meeting, 2019
T. Urban, E. Ziegler, S. Pieper, et al · 2019
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MULAN: Multitask universal lesion analysis network for joint lesion detection, tagging, and segmentation
K. Yan, Y. Tang, Y. Peng, , et al · 2019
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Weakly supervised lesion co-segmentation on CT scans
V. Agarwal, Y. Tang, J. Xiao, and R. M. Summers · 2020
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The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge
N. Heller, F. Isensee, K. H. Maier-Hein, et al · 2020
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European trends in radiology: investigating factors affecting the number of examinations and the effective dose
H. Masjedi, M. H. Zare, N. Keshavarz Siahpoush, et al · 2020
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The liver tumor segmentation benchmark (LiTS)
P. Bilic, P. Christ, H. B. Li, et al · 2022
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Monai: An open-source framework for deep learning in healthcare, 2022
M. J. Cardoso, W. Li, R. Brown, et al · 2022
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Early detection of cancer
D. Crosby, S. Bhatia, K. M. Brindle, et al · 2022
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Accurate and robust lesion RECIST diameter prediction and segmentation with transformers
Y. Tang, N. Zhang, Y. Wang, et al · 2022
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Lung cancer screening
S. J. Adams, E. Stone, D. R. Baldwin, et al · 2023
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Automatic segmentation of pancreas and pancreatic tumor: A review of a decade of research
H. Ghorpade, J. Jagtap, S. Patil, et al · 2023
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Patients undergoing recurrent CT scans: assessing the magnitude
M. M. Rehani, K. Yang, E. R. Melick, et al · 2020
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One click lesion RECIST measurement and segmentation on CT scans
Y. Tang, K. Yan, J. Xiao, and R. M. Summers · 2020
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Deep lesion tracker: Monitoring lesions in 4d longitudinal imaging studies
J. Cai, Y. Tang, K. Yan, et al · 2021
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Whole-body soft-tissue lesion tracking and segmentation in longitudinal CT imaging studies
A. Hering, F. Peisen, T. Amaral, et al · 2021
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nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
F. Isensee, P. F. Jaeger, S. A. Kohl, et al · 2021
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LNDb challenge on automatic lung cancer patient management
J. Pedrosa, G. Aresta, C. Ferreira, et al · 2021
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Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
H. Sung, J. Ferlay, R. L. Siegel, et al · 2021
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The KiTS21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT, 2023
N. Heller, F. Isensee, D. Trofimova, et al · 2023
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Segment anything model for medical image analysis: an experimental study
M. A. Mazurowski, H. Dong, H. Gu, et al · 2023
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RECIST-induced reliable learning: Geometry-driven label propagation for universal lesion segmentation
L. Zhou, L. Yu, and L. Wang · 2023
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The ULS23 baseline model
M. de Grauw, B. van Ginneken, and A. Hering · 2024
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Improving assessment of lesions in longitudinal ct scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow
A. Hering, M. Westphal, A. Gerken, et al · 2024
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nnU-Net revisited: A call for rigorous validation in 3D medical image segmentation, 2024
F. Isensee, T. Wald, C. Ulrich, et al · 2024
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Segment anything in medical images
J. Ma, Y. He, F. Li, L. Han, et al · 2024
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Grand-Challenge.org (v2024.01)
J. Meakin, P. K. Gerke, S. Kerkstra, et al · 2024
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