Access the full text.
Sign up today, get DeepDyve free for 14 days.
Patrick Bilic, P. Christ, Eugene Vorontsov, G. Chlebus, Hao Chen, Q. Dou, Chi-Wing Fu, Xiao Han, P. Heng, J. Hesser, S. Kadoury, Tomasz Konopczynski, Miao Le, Chunming Li, X. Li, Jana Lipková, J. Lowengrub, H. Meine, J. Moltz, C. Pal, M. Piraud, Xiaojuan Qi, Jin Qi, M. Rempfler, Karsten Roth, A. Schenk, A. Sekuboyina, Ping Zhou, Christian Hülsemeyer, M. Beetz, Florian Ettlinger, Felix Grün, Georgios Kaissis, F. Lohöfer, R. Braren, J. Holch, Felix Hofmann, W. Sommer, V. Heinemann, C. Jacobs, G. Mamani, B. Ginneken, G. Chartrand, A. Tang, M. Drozdzal, Avi Ben-Cohen, E. Klang, M. Amitai, E. Konen, H. Greenspan, Johan Moreau, A. Hostettler, L. Soler, R. Vivanti, Adi Szeskin, N. Lev-Cohain, J. Sosna, Leo Joskowicz, Bjoern Menze, ZENGMING SHEN (2019)
The Liver Tumor Segmentation Benchmark (LiTS)Medical image analysis, 84
Ke Yan, Le Lu, R. Summers (2017)
Unsupervised body part regression via spatially self-ordering convolutional neural networks2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
O. Ronneberger, P. Fischer, T. Brox (2015)
U-Net: Convolutional Networks for Biomedical Image SegmentationArXiv, abs/1505.04597
Amber Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. Ginneken, A. Kopp-Schneider, B. Landman, G. Litjens, Bjoern Menze, O. Ronneberger, R. Summers, Patrick Bilic, P. Christ, R. Do, M. Gollub, Jennifer Golia-Pernicka, S. Heckers, W. Jarnagin, M. McHugo, S. Napel, Eugene Vorontsov, L. Maier-Hein, M. Cardoso (2019)
A large annotated medical image dataset for the development and evaluation of segmentation algorithmsArXiv, abs/1902.09063
S. Pomerantz, Charles White, Thorsten Krebs, Barry Daly, Sathi Sukumar, Frank Hooper, Eliot Siegel (2000)
Liver and bone window settings for soft-copy interpretation of chest and abdominal CT.AJR. American journal of roentgenology, 174 2
Yuankai Huo, Zhoubing Xu, S. Bao, Camilo Bermúdez, Hyeonsoo Moon, P. Parvathaneni, T. Moyo, M. Savona, A. Assad, R. Abramson, B. Landman (2018)
Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional NetworksIEEE Transactions on Medical Imaging, 38
Hyunkwang Lee, Fabian Troschel, Shahein Tajmir, Georg Fuchs, Julia Mario, F. Fintelmann, Synho Do (2017)
Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric AnalysisJournal of Digital Imaging, 30
K. Sahi, S. Jackson, E. Wiebe, G. Armstrong, S. Winters, Ronald Moore, G. Low (2014)
The Value of “Liver Windows” Settings in the Detection of Small Renal Cell Carcinomas on Unenhanced Computed TomographyCanadian Association of Radiologists Journal, 65
(2015)
Multi-atlas labeling beyond the cranial vault
Diederik Kingma, Jimmy Ba (2014)
Adam: A Method for Stochastic OptimizationCoRR, abs/1412.6980
D. Bowden, A. Cox, B. Freedman, Christina Hugenschimdt, L. Wagenknecht, D. Herrington, S. Agarwal, T. Register, J. Maldjian, M. Ng, F. Hsu, C. Langefeld, J. Williamson, J. Carr (2010)
Review of the Diabetes Heart Study (DHS) family of studies: a comprehensively examined sample for genetic and epidemiological studies of type 2 diabetes and its complications.The review of diabetic studies : RDS, 7 3
Ke Yan, Le Lu, R. Summers (2017)
Unsupervised body part regression using convolutional neural network with self-organizationArXiv, abs/1707.03891
D. Bowden, A. Cox, B. Freedman, Christina Hugenschimdt, L. Wagenknecht, D. Herrington, S. Agarwal, T. Register, Joseph, A. Maldjian, M. Ng, F. Hsu, C. Langefeld, J. Williamson, J., J. Carr (2011)
A Comprehensively Examined Sample for Genetic and Epide- miological Studies of Type 2 Diabetes and its Complications
Sabrina Dorn, Shuqing Chen, S. Sawall, D. Simons, M. May, J. Maier, M. Knaup, H. Schlemmer, A. Maier, M. Lell, M. Kachelriess (2018)
Organ-specific context-sensitive CT image reconstruction and display, 10573
Abstract.Tissue window filtering has been widely used in deep learning for computed tomography (CT) image analyses to improve training performance (e.g., soft tissue windows for abdominal CT). However, the effectiveness of tissue window normalization is questionable since the generalizability of the trained model might be further harmed, especially when such models are applied to new cohorts with different CT reconstruction kernels, contrast mechanisms, dynamic variations in the acquisition, and physiological changes. We evaluate the effectiveness of both with and without using soft tissue window normalization on multisite CT cohorts. Moreover, we propose a stochastic tissue window normalization (SWN) method to improve the generalizability of tissue window normalization. Different from the random sampling, the SWN method centers the randomization around the soft tissue window to maintain the specificity for abdominal organs. To evaluate the performance of different strategies, 80 training and 453 validation and testing scans from six datasets are employed to perform multiorgan segmentation using standard 2D U-Net. The six datasets cover the scenarios, where the training and testing scans are from (1) same scanner and same population, (2) same CT contrast but different pathology, and (3) different CT contrast and pathology. The traditional soft tissue window and nonwindowed approaches achieved better performance on (1). The proposed SWN achieved general superior performance on (2) and (3) with statistical analyses, which offers better generalizability for a trained model.
Journal of Medical Imaging – SPIE
Published: Oct 1, 2019
Keywords: tissue window; computed tomography; deep learning; segmentation
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.