Quantifying the unknown impact of segmentation uncertainty on image-based simulations

Krygier, Michael C.; LaBonte, Tyler; Martinez, Carianne; Norris, Chance; Sharma, Krish; Collins, Lincoln N.; Mukherjee, Partha P.; Roberts, Scott A.

doi:10.1038/s41467-021-25493-8

Title: Quantifying the unknown impact of segmentation uncertainty on image-based simulations

Journal Article · Tue Sep 14 00:00:00 EDT 2021 · Nature Communications

DOI:https://doi.org/10.1038/s41467-021-25493-8· OSTI ID:1819983

;

; Martinez, Carianne;

; Sharma, Krish;

; Mukherjee, Partha P.;

Abstract Image-based simulation, the use of 3D images to calculate physical quantities, relies on image segmentation for geometry creation. However, this process introduces image segmentation uncertainty because different segmentation tools (both manual and machine-learning-based) will each produce a unique and valid segmentation. First, we demonstrate that these variations propagate into the physics simulations, compromising the resulting physics quantities. Second, we propose a general framework for rapidly quantifying segmentation uncertainty. Through the creation and sampling of segmentation uncertainty probability maps, we systematically and objectively create uncertainty distributions of the physics quantities. We show that physics quantity uncertainty distributions can follow a Normal distribution, but, in more complicated physics simulations, the resulting uncertainty distribution can be surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.

View Journal Article

Cite

Export

Save

Research Organization:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: NA0003525

OSTI ID:: 1819983

Alternate ID(s):: OSTI ID: 1828016

Report Number(s):: SAND-2021-10474J; 5414; PII: 25493

Journal Information:: Nature Communications, Journal Name: Nature Communications Vol. 12 Journal Issue: 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (52)

Electromechanical vortex filaments during cardiac fibrillation Christoph, J.; Chebbok, M.; Richter, C. Nature, Vol. 555, Issue 7698 https://doi.org/10.1038/nature26001	journal	February 2018
Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods Wei, Han; Zhao, Shuaishuai; Rong, Qingyuan International Journal of Heat and Mass Transfer, Vol. 127 https://doi.org/10.1016/j.ijheatmasstransfer.2018.08.082	journal	December 2018
Deep learning analysis on microscopic imaging in materials science Ge, M.; Su, F.; Zhao, Z. Materials Today Nano, Vol. 11 https://doi.org/10.1016/j.mtnano.2020.100087	journal	August 2020
Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation Kwon, Yongchan; Won, Joong-Ho; Kim, Beom Joon Computational Statistics & Data Analysis, Vol. 142 https://doi.org/10.1016/j.csda.2019.106816	journal	February 2020
Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes Jiang, Zhisen; Li, Jizhou; Yang, Yang Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-16233-5	journal	May 2020
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Milletari, Fausto; Navab, Nassir; Ahmadi, Seyed-Ahmad 2016 Fourth International Conference on 3D Vision (3DV) https://doi.org/10.1109/3DV.2016.79	conference	October 2016
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric Uncertainty LaBonte, Tyler Matthew; Martinez, Carianne; Roberts, Scott Alan https://doi.org/10.2172/1605518	report	March 2020
Numerical Simulation for Heat Transfer in Prostate Cancer Cryosurgery Zhang, Jiayao; Sandison, George A.; Murthy, Jayathi Y. Journal of Biomechanical Engineering, Vol. 127, Issue 2 https://doi.org/10.1115/1.1865193	journal	September 2004
ImageNet Large Scale Visual Recognition Challenge Russakovsky, Olga; Deng, Jia; Su, Hao International Journal of Computer Vision, Vol. 115, Issue 3 https://doi.org/10.1007/s11263-015-0816-y	journal	April 2015
Micro-computed tomography pore-scale study of flow in porous media: Effect of voxel resolution Shah, S. M.; Gray, F.; Crawshaw, J. P. Advances in Water Resources, Vol. 95 https://doi.org/10.1016/j.advwatres.2015.07.012	journal	September 2016
Current status and future trends of clinical diagnoses via image-based deep learning Xu, Jie; Xue, Kanmin; Zhang, Kang Theranostics, Vol. 9, Issue 25 https://doi.org/10.7150/thno.38065	journal	January 2019
Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization Jafargholi Rangraz, Esmaeel; Coudyzer, Walter; Maleux, Geert EJNMMI Research, Vol. 9, Issue 1 https://doi.org/10.1186/s13550-019-0485-x	journal	February 2019
Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection Madireddy, Sandeep; Chung, Ding-Wen; Loeffler, Troy Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-56649-8	journal	December 2019
ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning Rączkowski, Łukasz; Możejko, Marcin; Zambonelli, Joanna Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-50587-1	journal	October 2019
Leveraging uncertainty information from deep neural networks for disease detection Leibig, Christian; Allken, Vaneeda; Ayhan, Murat Seçkin Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-17876-z	journal	December 2017
Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data Baveye, Philippe C.; Laba, Magdeline; Otten, Wilfred Geoderma, Vol. 157, Issue 1-2 https://doi.org/10.1016/j.geoderma.2010.03.015	journal	June 2010
The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles Glaessgen, Edward; Stargel, David 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA https://doi.org/10.2514/6.2012-1818	conference	June 2012
Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR Trebeschi, Stefano; van Griethuysen, Joost J. M.; Lambregts, Doenja M. J. Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-05728-9	journal	July 2017
Disentangling astroglial physiology with a realistic cell model in silico Savtchenko, Leonid P.; Bard, Lucie; Jensen, Thomas P. Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05896-w	journal	September 2018
X-ray computed tomography of polymer composites Garcea, S. C.; Wang, Y.; Withers, P. J. Composites Science and Technology, Vol. 156 https://doi.org/10.1016/j.compscitech.2017.10.023	journal	March 2018
Determining the uncertainty in microstructural parameters extracted from tomographic data Pietsch, P.; Ebner, M.; Marone, F. Sustainable Energy & Fuels, Vol. 2, Issue 3 https://doi.org/10.1039/C7SE00498B	journal	January 2018
Reengineering Aircraft Structural Life Prediction Using a Digital Twin Tuegel, Eric J.; Ingraffea, Anthony R.; Eason, Thomas G. International Journal of Aerospace Engineering, Vol. 2011 https://doi.org/10.1155/2011/154798	journal	January 2011
A verified conformal decomposition finite element method for implicit, many-material geometries Roberts, Scott A.; Mendoza, Hector; Brunini, Victor E. Journal of Computational Physics, Vol. 375 https://doi.org/10.1016/j.jcp.2018.08.022	journal	December 2018
Sampling image segmentations for uncertainty quantification Lê, Matthieu; Unkelbach, Jan; Ayache, Nicholas Medical Image Analysis, Vol. 34 https://doi.org/10.1016/j.media.2016.04.005	journal	December 2016
No-Reference Image Quality Assessment in the Spatial Domain Mittal, A.; Moorthy, A. K.; Bovik, A. C. IEEE Transactions on Image Processing, Vol. 21, Issue 12 https://doi.org/10.1109/TIP.2012.2214050	journal	December 2012
Survey over image thresholding techniques and quantitative performance evaluation Sankur, Bu¨lent Journal of Electronic Imaging, Vol. 13, Issue 1 https://doi.org/10.1117/1.1631315	journal	January 2004
Segmentation of X-ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures: X-RAY CT IMAGE SEGMENTATION Iassonov, Pavel; Gebrenegus, Thomas; Tuller, Markus Water Resources Research, Vol. 45, Issue 9 https://doi.org/10.1029/2009WR008087	journal	September 2009
Computational geometry analysis of dendritic spines by structured illumination microscopy Kashiwagi, Yutaro; Higashi, Takahito; Obashi, Kazuki Nature Communications, Vol. 10, Issue 1 https://doi.org/10.1038/s41467-019-09337-0	journal	March 2019
Three-dimensional electrochemical Li-ion battery modelling featuring a focused ion-beam/scanning electron microscopy based three-phase reconstruction of a LiCoO2 cathode Hutzenlaub, T.; Thiele, S.; Paust, N. Electrochimica Acta, Vol. 115 https://doi.org/10.1016/j.electacta.2013.10.103	journal	January 2014
The Sensitivity of Estimates of Multiphase Fluid and Solid Properties of Porous Rocks to Image Processing Garfi, Gaetano; John, Cédric M.; Berg, Steffen Transport in Porous Media, Vol. 131, Issue 3 https://doi.org/10.1007/s11242-019-01374-z	journal	December 2019
Estimating uncertainty in MRF-based image segmentation: A perfect-MCMC approach Awate, Suyash P.; Garg, Saurabh; Jena, Rohit Medical Image Analysis, Vol. 55 https://doi.org/10.1016/j.media.2019.04.014	journal	July 2019
Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020) Alizadehsani, Roohallah; Roshanzamir, Mohamad; Hussain, Sadiq Annals of Operations Research https://doi.org/10.1007/s10479-021-04006-2	journal	March 2021
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification Arganda-Carreras, Ignacio; Kaynig, Verena; Rueden, Curtis Bioinformatics, Vol. 33, Issue 15 https://doi.org/10.1093/bioinformatics/btx180	journal	March 2017
3D microstructure design of lithium-ion battery electrodes assisted by X-ray nano-computed tomography and modelling Lu, Xuekun; Bertei, Antonio; Finegan, Donal P. Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-15811-x	journal	April 2020
Interactive volumetric segmentation for textile micro‐tomography data using wavelets and nonlocal means MacNeil, J. Michael L.; Ushizima, Daniela M.; Panerai, Francesco Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 12, Issue 4 https://doi.org/10.1002/sam.11429	journal	May 2019
Anisotropic analysis of fibrous and woven materials part 2: Computation of effective conductivity Semeraro, Federico; Ferguson, Joseph C.; Acin, Marcos Computational Materials Science, Vol. 186 https://doi.org/10.1016/j.commatsci.2020.109956	journal	January 2021
Blind/Referenceless Image Spatial Quality Evaluator Mittal, Anish; Moorthy, Anush K.; Bovik, Alan C. 2011 45th Asilomar Conference on Signals, Systems and Computers, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR) https://doi.org/10.1109/ACSSC.2011.6190099	conference	November 2011
Quantifying the Unknown: Impact of Segmentation Uncertainty on Image-Based Simulations - Data Roberts, Scott Mendeley https://doi.org/10.17632/g3hr4rkb48.3	dataset	January 2021
Spatial Uncertainty Modeling for Surface Roughness of Additively Manufactured Microstructures via Image Segmentation Kim, Namjung; Yang, Chen; Lee, Howon Applied Sciences, Vol. 9, Issue 6 https://doi.org/10.3390/app9061093	journal	March 2019
Stochastic characterisation methodology for 3-D textiles based on micro-tomography Vanaerschot, Andy; Panerai, Francesco; Cassell, Alan Composite Structures, Vol. 173 https://doi.org/10.1016/j.compstruct.2017.03.107	journal	August 2017
Random Walks for Image Segmentation Grady, L. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, Issue 11 https://doi.org/10.1109/TPAMI.2006.233	journal	November 2006
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing 2015 IEEE International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV.2015.123	conference	December 2015
Quantifying Inhomogeneity of Lithium Ion Battery Electrodes and Its Influence on Electrochemical Performance Müller, Simon; Eller, Jens; Ebner, Martin Journal of The Electrochemical Society, Vol. 165, Issue 2 https://doi.org/10.1149/2.0311802jes	journal	January 2018
Mechanical and Electrochemical Response of a LiCoO2 Cathode using Reconstructed Microstructures Mendoza, Hector; Roberts, Scott A.; Brunini, Victor E. Electrochimica Acta, Vol. 190 https://doi.org/10.1016/j.electacta.2015.12.224	journal	February 2016
Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics Hoeijmakers, M. J. M. M.; Silva Soto, D. A.; Waechter-Stehle, I. Journal of Biomechanics, Vol. 94 https://doi.org/10.1016/j.jbiomech.2019.07.010	journal	September 2019
Low Wall Shear Stress Predominates at Sites of Abdominal Aortic Aneurysm Rupture Boyd, April J.; Kuhn, David C. S.; Lozowy, Richard J. Journal of Vascular Surgery, Vol. 62, Issue 5 https://doi.org/10.1016/j.jvs.2015.08.041	journal	November 2015
Image‐based modeling of tumor shrinkage in head and neck radiation therapya) Chao, Ming; Xie, Yaoqin; Moros, Eduardo G. Medical Physics, Vol. 37, Issue 5 https://doi.org/10.1118/1.3399872	journal	April 2010
Secondary-Phase Stochastics in Lithium-Ion Battery Electrodes Mistry, Aashutosh N.; Smith, Kandler; Mukherjee, Partha P. ACS Applied Materials & Interfaces, Vol. 10, Issue 7 https://doi.org/10.1021/acsami.7b17771	journal	January 2018
Digital twin-driven product design, manufacturing and service with big data Tao, Fei; Cheng, Jiangfeng; Qi, Qinglin The International Journal of Advanced Manufacturing Technology, Vol. 94, Issue 9-12 https://doi.org/10.1007/s00170-017-0233-1	journal	March 2017
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises Zhou, S. Kevin; Greenspan, Hayit; Davatzikos, Christos Proceedings of the IEEE, Vol. 109, Issue 5 https://doi.org/10.1109/JPROC.2021.3054390	journal	May 2021
Simulation of blood flow in human aorta with emphasis on outlet boundary conditions Benim, A. C.; Nahavandi, A.; Assmann, A. Applied Mathematical Modelling, Vol. 35, Issue 7 https://doi.org/10.1016/j.apm.2010.12.022	journal	July 2011
The Role of Molecular Imaging in Drug Discovery and Development Hargreaves, Rj Clinical Pharmacology & Therapeutics, Vol. 83, Issue 2 https://doi.org/10.1038/sj.clpt.6100467	journal	January 2008

Similar Records

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Journal Article · Wed May 31 00:00:00 EDT 2023 · Computational Mechanics · OSTI ID:1819983

Martinez, Carianne; Bolintineanu, Dan S.; Olson, Aaron; +7 more

Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm

Journal Article · Thu Jan 15 00:00:00 EST 2015 · Medical Physics · OSTI ID:1819983

Veiga, Catarina; Lourenço, Ana Mónica; Mouinuddin, Syed; +5 more

Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks

Journal Article · Wed Sep 19 00:00:00 EDT 2018 · Medical Physics · OSTI ID:1819983

Tong, Nuo; Gou, Shuiping; Yang, Shuyuan; +2 more

Related Subjects

42 ENGINEERING

Title: Quantifying the unknown impact of segmentation uncertainty on image-based simulations

Citation Formats

References (52)

Similar Records

Related Subjects