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Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images

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Abstract

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

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References

  1. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, Zegers CML, Gillies R, Boellard R, Dekker A et al.: Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446, 2012

    Article  PubMed  PubMed Central  Google Scholar 

  2. Aerts HJWL, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P, Cavalho S, Bussink J et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006, 2014

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJWL, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ et al.: Radiomics: The process and the challenges. Magn Reson Imaging [Internet] 30(9):1234–1248, 2012. https://doi.org/10.1016/j.mri.2012.06.010

    Article  Google Scholar 

  4. Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RTH, Hermann G, Lambin P, Haibe-Kains B, Mak RH, Aerts HJWL: CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol [Internet] 114(3):345–350, 2015. https://doi.org/10.1016/j.radonc.2015.02.015

    Article  Google Scholar 

  5. Parmar C, Velazquez ER, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Mitra S, Shankar BU, Kikinis R, Haibe-Kains B, Lambin P, Aerts HJWL: Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9(7):1–8, 2014

    Article  CAS  Google Scholar 

  6. Gatenby RA, Grove O, Gillies RJ: Quantitative imaging in cancer evolution and ecology. Radiology [Internet] 269(1):8–15, 2013 Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3781355&tool=pmcentrez&rendertype=abstract

    Article  Google Scholar 

  7. Leijenaar RTH, Carvalho S, Velazquez ER, van Elmpt WJC, Parmar C, Hoekstra OS, Hoekstra CJ, Boellaard R, Dekker A, Gillies RJ, Aerts HJWL, Lambin P: Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability. Acta Oncol (Madr) [Internet] 52(7):1391–1397, 2013 Available from: http://www.ncbi.nlm.nih.gov/pubmed/24047337

    Article  CAS  Google Scholar 

  8. Machine Learning | Microsoft Azure [Internet]. Available from: https://azure.microsoft.com/en-us/services/machine-learning/

  9. Ludäscher B, Altintas I, Berkley C, Higgins D, Jaeger E, Jones M, Lee EA, Tao J, Zhao Y: Scientific workflow management and the Kepler system. Concurr Comput Pract Exp 18(10):1039–1065, 2006

    Article  Google Scholar 

  10. Parker SG, Johnson CR: SCIRun: A Scientific Programming Environment for Computational Steering [Internet]. In: Proceedings of the 1995 ACM/IEEE Conference on Supercomputing. New York: ACM, 1995. Available from: http://doi.acm.org/10.1145/224170.224354

  11. Hull D, Wolstencroft K, Stevens R, Goble C, Pocock MR, Li P, Oinn T: Taverna: A tool for building and running workflows of services. Nucleic Acids Res 34(WEB. SERV. ISS):729–732, 2006

    Article  CAS  Google Scholar 

  12. Taylor I, Shields M, Wang I, Harrison A: The triana workflow environment: Architecture and applications. Work e-Science Sci Work Grids:320–339, 2007

  13. Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE: IBEX: An open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys [Internet] 42:1341–1353, 2015 Available from: http://scitation.aip.org/content/aapm/journal/medphys/42/3/10.1118/1.4908210

    Article  Google Scholar 

  14. van Griethuysen J, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan R, Fillion-Robin JC, Pieper S, Aerts HJWL: Computational Radiomics System to Decode the Radiographic Phenotype. Accepted Cancer Res 2017. https://github.com/Radiomics/pyradiomics

  15. Boettiger C: An introduction to Docker for reproducible research. ACM SIGOPS Oper Syst Rev [Internet] 49(1):71–79, 2015 Available from: http://arxiv.org/abs/1410.0846

    Article  Google Scholar 

  16. Ince DC, Hatton L, Graham-Cumming J: The case for open computer programs. Nature [Internet] 482(7386):485–488, 2012 Available from: http://www.ncbi.nlm.nih.gov/pubmed/22358837

    Article  CAS  Google Scholar 

  17. Bitzer J, Schröder PJH: Bug-fixing and code-writing: The private provision of open source software. Inf Econ Policy 17(3):389–406, 2005

    Article  Google Scholar 

  18. Aberdour M: Achieving quality in open source software. IEEE Softw [Internet] (September):58–64, 2007 Available from: http://www.computer.org/portal/web/csdl/doi/10.1109/MS.2007.2

  19. Simmhan YL, Plale B, Gannon D: A survey of data provenance in e-science [internet]. SIGMOD Rec. 34(3):31–36, 2005 Available from: http://doi.acm.org/10.1145/1084805.1084812%5C, http://dl.acm.org/ft_gateway.cfm?id=1084812&type=pdf

    Article  Google Scholar 

  20. Davidson SB, Freire J: Provenance and scientific workflows. Proc 2008 ACM SIGMOD Int Conf Manag data - SIGMOD ‘08 [Internet], 2008, p 1345. Available from: http://www.scopus.com/inward/record.url?eid=2-s2.0-57149126952&partnerID=tZOtx3y1

  21. Mildenberger P, Eichelberg M, Martin E: Introduction to the DICOM standard. Eur Radiol 12(4):920–927, 2002

    Article  PubMed  Google Scholar 

  22. DICOM Standards Committee WG 17 (3D). Supplement 111: Segmentation Storage SOP Class. In: Digital Imaging and Communications in Medicine (DICOM). Rosslyn, Virginia, 2006, p 22209

  23. Liu B, Zhu M, Zhang Z, Yin C, Liu Z, Gu J: Medical image conversion with DICOM. Can Conf Electr Comput Eng:36–39, 2007

  24. Riesmeier J, Eichelberg M, Jensch P: An approach to DICOM image display handling the full flexibility of the standard’s specification. Med Imaging 1999 Image Disp 3658(February):363–9, 1999

  25. Jonker PP: Morphological operations on 3D and 4D images: From shape primitive detection to skeletonization. In: Lecture Notes in Computer Science. 2000, pp 371–91

  26. Norris N: General means and statistical theory. Am Stat [Internet] 30(1):8–12, 1976 Available from: http://www.tandfonline.com/doi/abs/10.1080/00031305.1976.10479125

    Google Scholar 

  27. Mathworks. isosurface [Internet]. Matlab Ref. [cited 2016 Oct 19]. Available from: https://www.mathworks.com/help/matlab/ref/isosurface.html

  28. reducepatch [Internet]. Mathworks MATLAB 2016a Doc. Available from: https://www.mathworks.com/help/matlab/ref/reducepatch.html

  29. Han J, Moraga C: The influence of the sigmoid function parameters on the speed of backpropagation learning. From Nat to Artif Neural Comput [Internet] 930:195–201, 1995. doi:https://doi.org/10.1007/3-540-59497-3_175

  30. Xu J, Napel S, Greenspan H, Beaulieu CF, Agrawal N, Rubin D: Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval. Med Phys 39(9):5405–5418, 2012

    Article  PubMed  PubMed Central  Google Scholar 

  31. nlinfit [Internet]. Mathworks MATLAB 2016a Doc.2016. Available from: https://www.mathworks.com/help/stats/nlinfit.html

  32. Degarmo EP, Black J, Kohser RA: Materials and processes in manufacturing, 9th edition. Hoboken: Wiley, 2003

    Google Scholar 

  33. Definition and Designation of Surface Roughness. JIS B 0601. Japanese Industrial Standard, 1982

  34. Surface Texture Symbols [Internet]. The American Society of Mechanical Engineers, 1996. Available from: https://www.asme.org/products/codes-standards/y1436m-1996-surface-texture-symbols

  35. Wadell H: Volume, shape, and roundness of quartz particles. J Geol [Internet] 43(3):250–280, 1935 Available from: http://www.journals.uchicago.edu/doi/10.1086/624298

    Article  Google Scholar 

  36. Haralick RMM, Shanmugam K, Dinstein IH: Textural Features for Image Classification. IEEE Trans Syst Man Cybern [Internet] [cited 2010 Nov 6];SMC-3(6):610–21, 1973. Available from: http://ieeexplore.ieee.org/xpls/abs_all.jsp?&arnumber=4309314

  37. Kong TY, Roscoe AW, Rosenfeld A: Concepts of digital topology. Topol Appl [Internet] 46(3):219–262, 1992 Available from: http://www.sciencedirect.com/science/article/pii/016686419290016S

    Article  Google Scholar 

  38. Shafranovich, Y.: Common Format and MIME Type for Comma-Separated Values (CSV) File, RFC 4180, October 2005. https://tools.ietf.org/html/rfc4180. Accessed 2017-05-01

  39. Opensource.org. The BSD 2-Clause License [Internet]. Licenses 2016.Available from: https://opensource.org/licenses/BSD-2-Clause

  40. Echegaray S, Nair V, Kadoch M, Leung A, Rubin D, Gevaert O, Napel S: A rapid segmentation-insensitive “digital biopsy” method for Radiomic feature extraction: Method and pilot study using CT images of non–small cell lung cancer. Tomography [Internet] 2(4):283–294, 2016. Available from: http://digitalpub.tomography.org/i/763956-vol-2-no-4-dec-2016/52

    Article  Google Scholar 

  41. Kalpathy-Cramer J, Mamomov A, Zhao B, Lu L, Cherezov D, Napel S, Echegaray S, McNitt-Gray M, Lo P, Sieren JC, Uthoff J, Dilger SKN, Driscoll B, Yeung I, Goldgof D: Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features. Tomography 2(4):430–437, 2016. https://doi.org/10.18383/j.tom.2016.00235

  42. Napel SA, Beaulieu CF, Rodriguez C, Cui J, Xu J, Gupta A, Korenblum D, Greenspan H, Ma Y, Rubin DL: Automated retrieval of CT images of liver lesions on the basis of image similarity: Method and preliminary results. Radiology [Internet] 256(1):243–252, 2010. https://doi.org/10.1148/radiol.10091694

    Article  Google Scholar 

  43. Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, Cheshier SH, Napel S, Zaharchuk G, Plevritis SK: Glioblastoma Multiforme: Exploratory Radiogenomic analysis by using quantitative image features. Radiology [Internet] 273(1):168–174, 2014. https://doi.org/10.1148/radiol.14131731

    Article  Google Scholar 

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Acknowledgements

This research was funded in part by the following grants from the National Institutes of Health: R01 CA160251, U24 CA180927, U01 CA187947, and U01-CA190214.

Funding

This work was supported by the National Institutes of Health Grants R01 CA160251, U01 CA187947, U01-CA190214, and U24 CA180927.

Author information

Authors and Affiliations

  1. Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA

    Sebastian Echegaray, Daniel L. Rubin & Sandy Napel

  2. Department of Electrical Engineering, Stanford University, 650 Serra Mall, Stanford, CA, 94305, USA

    Shaimaa Bakr

  3. Department of Medicine (Biomedical Informatics Research), Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA

    Daniel L. Rubin

Authors
  1. Sebastian Echegaray
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  4. Sandy Napel
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Correspondence to Sebastian Echegaray.

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Conflict of interest

Dr. Napel is a consultant for Carestream, Inc. and is on the scientific advisory boards of Echo Pixel, Inc., Fovia, Inc., and RadLogics, Inc.

Appendices

Appendix 1 Configuration Parameters for each component

Global

Parameter name

Default Value

Description

inputRoot

N/A

Root directory for input. (All input folders are relative to this directory)

outputRoot

N/A

Root directory for output. (All output folders are relative to this directory)

parallelMode

none

What parallelization strategy to use (None, Object, Feature, Internal)

numberOfProcessors

max

If parallelMode is other than None, then the software creates a processing pool with numberOfProcessors processors. “Max” uses all available

uidToProcess

all

A list of UID to be processed by the QIFE. If “all” it processes all volumes loaded by the input stage

Input Stage

DSO/DICOM loader component

Parameter name

Default value

Description

dicomFolder

N/A

Folder relative to inputRoot where the DICOM sets are stored

dsoFolder

N/A

Folder relative to inputRoot where the DSOs are stored

recomputeHashTable

false

Computes the UID hash tables even if a cache index is found in the directory

saveHashTable

true

Saves a cache of the UID hashtables in their root directories

padding

10

Millimeters to go outside the VOI when loading the VOI

Preprocessing stage

Segmentation deformation

Parameter name

Default value

Description

operation

N/A

Operation to perform in the VOI (“erosion” or “dilation”).

sizeOfElement

N/A

Size of the ball used to perform the operation specified.

Topology preservation

Parameter Name

Default value

Description

sizeOfGap

N/A

Maximum size of gaps to be bridged.

Maximum connected volume selection

Parameter name

Default value

Description

connectivity

26

What connectivity determines that a voxel is part of the same volume (Possible values: 6, 18, 26)

Hole filling

Parameter name

Default value

Description

connectivity

26

What connectivity determines that a voxel is part of the same volume (Possible values: 6, 18, 26)

Feature Computation Stage

Size distribution features

Parameter name

Default value

Description

featureRootName

size

The prefix to add to all results generated by this component

Intensity distribution features

Parameter name

Default value

Description

featureRootName

intensity

The prefix to add to all results generated by this component

Edge sharpness features

Parameter name

Default value

Description

featureRootName

edge

The prefix to add to all results generated by this component

normalLength

5

Length in millimeters of normals in each direction.

numberOfNormals

600

Number of normals after triangulation and decimation

numberOfSamplingPoints

21

Number of intensity samples along a normal

Local volume invariant integral (LVII) feature

Parameter name

Default value

Description

featureRootName

lvii

The prefix to add to all results generated by this component

sphereRadius

1,2,3,4,5

List of radii for the Sphere used to calculate intersections separated by commas

Roughness feature

Parameter name

Default value

Description

featureRootName

roughness

The prefix to add to all results generated by this component

patchSize

3

Maximum distance in mm for a voxel to be considered in the same patch when roughness is computed

Sphericity feature

Parameter name

Default value

Description

featureRootName

sphericity

The prefix to add to all results generated by this component

Haralick’s texture features

Parameter name

Default value

Description

featureRootName

haralick

The prefix to add to all results generated by this component

distance

1,2,3

Distances in mm at which to calculate the GLCM.

grayLevels

16

Number of gray levels to quantify intensity values to.

Output stage

CSV Exporter

Parameter name

Default value

Description

filename

out.csv

Filename for the csv file relative to out folder

Transpose

false

Transpose the data in the CSV (headers in the first column)

Run information exporter

Parameter name

Default value

Description

filename

Info.txt

Filename for the run information file

Cross-sectional image generator

Parameter name

Default value

Description

folderRoot

.

Folder relative to output root where to save the generated images. By default it uses the output root folder.

windowLevelPreset

ctLung

Window and Level preset. By default it assumes Lung CT

Reference Generator

Parameter name

Default value

Description

filename

references.bib

Filename for the bib file relative to out folder

Appendix 2 Example Configuration File

The configuration file defines which components are loaded in each stage, and can override default parameters (shown in Appendix 1). The file follows the following syntax:

Category|ParameterName = VALUE (The separator is a pipe “|”).

Category can be:

  • global (sets parameter for the whole engine)

  • input (sets parameter for the input stage)

  • preprocessing (sets parameters for the preprocessing stage)

  • featureComputation (sets parameters for the feature computation stage)

  • output (sets parameters for the output stage), or

  • a specific component name to override its defaults.

Multiple parameters can be set using comma as a separator.

Comments are defined with a semicolon at the beginning of a line. The following is an example configuration file:

  • ; Global Parameters

  • ; Disables parallel mode

  • global|parallelMode = “none”

  • ; Use the maximum number of processors

  • global|numberOfProcessors = “max”

  • ; Process all files included in the input directory

  • global|uidToProcess = “all”

  • ; Components to load

  • ; Input components to load

  • input|component = “dsoLoader”

  • ; Preprocessing components to load

  • preprocessing|components = “maximumConnected,holeFilling”

  • ; Feature computation components to load

  • featureComputation|components = “information,size,intensity,sphericity,roughness,edgeSigmoidFitting,lvii,glcm,connectedRegions”

  • ; Output components to load

  • output|components = “csvOutput,maxAreaImage,references”

  • ; Component parameters to override (See Appendix 1 for definition)

  • ; Number of Normals in the Edge Sigmoid Feature

  • edgeSigmoidFitting|numberOfNormals = 1200

  • ; Window and Level preset

  • maxAreaImage|windowLevelPreset = “ctLung”

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Echegaray, S., Bakr, S., Rubin, D.L. et al. Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images. J Digit Imaging 31, 403–414 (2018). https://doi.org/10.1007/s10278-017-0019-x

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