ImageJ2: ImageJ for the next generation of scientific image data

BMC Bioinformatics

Volumn 18, Issue 1, 2017, Pages

  Rueden, Curtis T ^a   Schindelin, Johannes ^a,b   Hiner, Mark C ^a   DeZonia, Barry E ^a   Walter, Alison E ^a,b   Arena, Ellen T ^a,b   Eliceiri, Kevin W ^a,b  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b MORGRIDGE INSTITUTE FOR RESEARCH   (United States)

Author keywords

Extensibility; Image processing; ImageJ; ImageJ2; Interoperability; N dimensional; Open development; Open source; Reproducibility

Indexed keywords

IMAGE PROCESSING; INTEROPERABILITY; SOFTWARE ENGINEERING; USER INTERFACES;

EXTENSIBILITY; IMAGEJ; IMAGEJ2; OPEN DEVELOPMENT; OPEN SOURCES; REPRODUCIBILITIES;

OPEN SOURCE SOFTWARE;

COMPUTER INTERFACE; HUMAN; IMAGE PROCESSING; PROCEDURES; REPRODUCIBILITY;

HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; USER-COMPUTER INTERFACE;

EID: 85035323246     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-017-1934-z     Document Type: Article

References (159)

1. Schneider CA, Rasband WS, Eliceiri KW, et al. Nih image to imagej: 25 years of image analysis. Nat Methods. 2012; 9(7):671-5.
2. Arena ET, Rueden CT, Hiner MC, Wang S, Yuan M, Eliceiri KW. Quantitating the cell: turning images into numbers with imagej. Wiley Interdiscip Rev Dev Biol. 2017; 6(2):e260. doi: 10.1002/wdev.260.
3. ImageJ Contributors. https://imagej.net/Contributors. Accessed 7 Aug 2017.
4. Peng H. Bioimage informatics: a new area of engineering biology. Bioinformatics. 2008; 24(17):1827-1836.
5. Eliceiri KW, Berthold MR, Goldberg IG, Ibáñez L, Manjunath BS, Martone ME, Murphy RF, Peng H, Plant AL, Roysam B, et al. Biological imaging software tools. Nat Methods. 2012; 9(7):697-710.
6. Swedlow JR, Eliceiri KW. Open source bioimage informatics for cell biology. Trends Cell Biol. 2009; 19(11):656-60.
7. Edelstein A, Amodaj N, Hoover K, Vale R, Stuurman N. Computer control of microscopes using μmanager. Hoboken: Wiley Online Library; 2010, pp. 14-20. doi: 10.1002/0471142727.mb1420s92.
8. Edelstein AD, Tsuchida MA, Amodaj N, Pinkard H, Vale RD, Stuurman N. Advanced methods of microscope control using μmanager software. J Biol Methods. 2014; 1(2):10. doi: 10.14440/jbm.2014.36.
9. Kvilekval K, Fedorov D, Obara B, Singh A, Manjunath B. Bisque: a platform for bioimage analysis and management. Bioinformatics. 2010; 26(4):544-52.
10. Allan C, Burel JM, Moore J, Blackburn C, Linkert M, Loynton S, MacDonald D, Moore WJ, Neves C, Patterson A, et al. Omero: flexible, model-driven data management for experimental biology. Nature Methods. 2012; 9(3):245-53.
11. De Chaumont F, Dallongeville S, Chenouard N, Hervé N, Pop S, Provoost T, Meas-Yedid V, Pankajakshan P, Lecomte T, Le Montagner Y, et al. Icy: an open bioimage informatics platform for extended reproducible research. Nature Methods. 2012; 9(7):690-6.
12. Kankaanpää P, Paavolainen L, Tiitta S, Karjalainen M, Päivärinne J, Nieminen J, Marjomäki V, Heino J, White DJ. Bioimagexd: an open, general-purpose and high-throughput image-processing platform. Nature Methods. 2012; 9(7):683-9.
13. Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, et al. Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 2006; 7(10):100.
14. Kamentsky L, Jones TR, Fraser A, Bray MA, Logan DJ, Madden KL, Ljosa V, Rueden C, Eliceiri KW, Carpenter AE. Improved structure, function and compatibility for cellprofiler: modular high-throughput image analysis software. Bioinformatics. 2011; 27(8):1179-1180.
15. Berthold MR, Cebron N, Dill F, Gabriel TR, Kötter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B. KNIME: The Konstanz Information Miner In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R, editors. Data Analysis, Machine Learning and Applications: Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7-9, 2007. Berlin: Springer: 2008. p. 319-326. doi: 10.1007/978-3-540-78246-9_38.
16. Dietz C, Berthold MR. Knime for open-source bioimage analysis: A tutorial. In: De Vos WH, Munck S, Timmermans J-P, editors. Focus on Bio-Image Informatics. Cham: Springer: 2016. p. 179-97. doi: 10.1007/978-3-319-28549-8_7.
17. Warr WA. Scientific workflow systems: Pipeline pilot and knime. J Comput Aided Mol Des. 2012; 26(7):801-4.
18. Wan Y, Otsuna H, Chien C-B, Hansen C. Fluorender: an application of 2d image space methods for 3d and 4d confocal microscopy data visualization in neurobiology research. In: Pacific Visualization Symposium (PacificVis), 2012 IEEE. Songdo: IEEE: 2012. p. 201-8. doi: 10.1109/PacificVis.2012.6183592.
19. Peng H, Bria A, Zhou Z, Iannello G, Long F. Extensible visualization and analysis for multidimensional images using vaa3d. Nat Protoc. 2014; 9(1):193-208.
20. Schindelin J, Rueden CT, Hiner MC, Eliceiri KW. The imagej ecosystem: An open platform for biomedical image analysis. Mol Reprod Dev. 2015; 82(7-8):518-29.
21. List of ImageJ Update Sites. https://imagej.net/List_of_update_sites. Accessed 17 Jan 2017.
22. SciJava. http://www.scijava.org/. Accessed 17 Jan 2017.
23. 2015 ImageJ Conference Presentation: Survey. https://imagej.github.io/presentations/2015-09-03-imagej2-and-fiji/#/6. Accessed 4 Aug 2017.
24. Carpenter AE, Kamentsky L, Eliceiri KW. A call for bioimaging software usability. Nature Methods. 2012; 9(7):666.
25. Atwood J. Hardware Is Cheap, Programmers Are Expensive. 2008. https://blog.codinghorror.com/hardware-is-cheap-programmers-are-expensive/. Accessed 17 Jan 2017.
26. SciJava. https://imagej.net/SciJava. Accessed 17 Jan 2017.
27. Pietzsch T, Preibisch S, Tomančák P, Saalfeld S. Imglib2-generic image processing in java. Bioinformatics. 2012; 28(22):3009-011.
28. Hiner MC, Rueden CT, Eliceiri KW. Scifio: an extensible framework to support scientific image formats. BMC Bioinformatics. 2016; 17(1):521.
29. ImageJ. https://imagej.net/. Accessed 17 Jan 2017.
30. ImageJ Architecture. https://imagej.net/Architecture. Accessed 17 Jan 2017.
31. SciJava Common. https://imagej.net/SciJava_Common. Accessed 17 Jan 2017.
32. Spring. https://spring.io/. Accessed 17 Jan 2017.
33. Dependency Injection. https://en.wikipedia.org/wiki/Dependency_injection. Accessed 17 Jan 2017.
34. Inversion of Control. https://en.wikipedia.org/wiki/Inversion_of_control. Accessed 17 Jan 2017.
35. Gruber J. Daring Fireball: Markdown. https://daringfireball.net/projects/markdown/. Accessed 17 Jan 2017.
36. ImageJ Common. https://imagej.net/ImageJ_Common. Accessed 17 Jan 2017.
37. Groovy. http://groovy-lang.org/. Accessed 7 Aug 2017.
38. Hiner MC, Rueden CT, Eliceiri KW. Imagej-matlab: a bidirectional framework for scientific image analysis interoperability. Bioinformatics. 2016; 33(4):629-630. doi: 10.1093/bioinformatics/btw681.
39. Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, Metaxas D, Whitaker R. Engineering and algorithm design for an image processing api: a technical report on itk-the insight toolkit. Stud Health Technol Inform. 2002; 85:586-592.
40. ImageJ-ITK. https://imagej.net/ITK. Accessed 17 Jan 2017.
41. ImageJ-OMERO. https://github.com/imagej/imagej-omero. Accessed 17 Jan 2017.
42. Goldberg IG, Allan C, Burel JM, Creager D, Falconi A, Hochheiser H, Johnston J, Mellen J, Sorger PK, Swedlow JR. The open microscopy environment (ome) data model and xml file: open tools for informatics and quantitative analysis in biological imaging. Genome Biol. 2005; 6(5):47.
43. ImageJ Tutorial Notebooks. https://imagej.github.io/tutorials/. Accessed 7 Aug 2017.
44. OpenCL. https://www.khronos.org/opencl/. Accessed 4 Aug 2017.
45. CUDA. http://www.nvidia.com/object/cuda_home_new.html. Accessed 4 Aug 2017.
46. Apache Spark. https://spark.apache.org/. Accessed 17 Jan 2017.
47. Chiba S, Nishizawa M. An easy-to-use toolkit for efficient java bytecode translators. In: International Conference on Generative Programming and Component Engineering. Erfurt: Springer: 2003. p. 364-76.
48. Feathers M. Working Effectively with Legacy Code. Upper Saddle River: Prentice Hall Professional; 2004.
49. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, et al. Fiji: an open-source platform for biological-image analysis. Nature Methods. 2012; 9(7):676-82.
50. Apache Maven. https://maven.apache.org/.
51. Linkert M, Rueden CT, Allan C, Burel JM, Moore W, Patterson A, Loranger B, Moore J, Neves C, MacDonald D, et al. Metadata matters: access to image data in the real world. J Cell Biol. 2010; 189(5):777-82.
52. Austenfeld M, Beyschlag W. A graphical user interface for r in a rich client platform for ecological modeling. J Stat Softw. 2012; 49(4):1-19.
53. Mongis C. ImageJFX - an Enhanced Interface for ImageJ. http://www.imagejfx.net/. Accessed 17 Jan 2017.
54. Preston-Werner T. The GitHub Effect: Forking Your Way to Better Code (FOWA Vegas 2011). 2011. http://lanyrd.com/2011/fowa-vegas/sfxcw/. Accessed 2 Apr 2016.
55. ImageJ Source Code. https://imagej.net/Source:code. Accessed 19 Jan 2017.
56. ImageJ Licensing. https://imagej.net/Licensing. Accessed 17 Jan 2017.
57. ImageJ Javadocs. https://javadoc.imagej.net/. Accessed 17 Jan 2017.
58. ImageJ Tutorials. https://imagej.net/Tutorials. Accessed 7 Aug 2017.
59. ImageJ Issue Management. https://imagej.net/Issues. Accessed 17 Jan 2017.
60. Contributing to ImageJ. https://imagej.net/Contributing. Accessed 17 Jan 2017.
61. Travis CI. https://travis-ci.org/. Accessed 7 Aug 2017.
62. Uber-JARs. https://imagej.net/Uber-JAR. Accessed 17 Jan 2017.
63. Personal Update Sites. https://sites.imagej.net/. Accessed 17 Jan 2017.
64. Hyde R. The fallacy of premature optimization. Ubiquity. 2009; 2009(February):1.
65. ImgLib, 2 Benchmarks. https://imagej.net/ImgLib2_Benchmarks. Accessed 17 Jan 2017.
66. Pietzsch T, Saalfeld S, Preibisch S, Tomancak P. Bigdataviewer: visualization and processing for large image data sets. Nature Methods. 2015; 12(6):481-3.
67. Tinevez JY, Perry N, Schindelin J, Hoopes GM, Reynolds GD, Laplantine E, Bednarek SY, Shorte SL, Eliceiri KW. Trackmate: An open and extensible platform for single-particle tracking. Methods. 2017; 115:80-90.
68. Cho NW, Lampson MA, Greenberg RA. In vivo imaging of dna double-strand break induced telomere mobility during alternative lengthening of telomeres. Methods. 2017; 114:54-9.
69. Wolff C, Tinevez JY, Pietzsch T, Stamataki E, Harich B, Preibisch S, Shorte S, Keller PJ, Tomancak P, Pavlopoulos A. Reconstruction of cell lineages and behaviors underlying arthropod limb outgrowth with multi-view light-sheet imaging and tracking. bioRxiv. 2017:112623.
70. Preibisch S, Saalfeld S, Schindelin J, Tomancak P. Software for bead-based registration of selective plane illumination microscopy data. Nature Methods. 2010; 7(6):418-9.
71. Preibisch S, Amat F, Stamataki E, Sarov M, Singer RH, Myers E, Tomancak P. Efficient bayesian-based multiview deconvolution. Nature Methods. 2014; 11(6):645-8.
72. Icha J, Schmied C, Sidhaye J, Tomancak P, Preibisch S, Norden C. Using light sheet fluorescence microscopy to image zebrafish eye development. Journal of visualized experiments: JoVE. 2016; 110:53966. doi: 10.3791/53966.
73. Fame RM, Chang JT, Hong A, Aponte-Santiago NA, Sive H. Directional cerebrospinal fluid movement between brain ventricles in larval zebrafish. Fluids and Barriers of the CNS. 2016; 13(1):11.
74. Ferreira TA, Blackman AV, Oyrer J, Jayabal S, Chung AJ, Watt AJ, Sjöström PJ, Van Meyel DJ. Neuronal morphometry directly from bitmap images. Nature Methods. 2014; 11(10):982-4.
75. Longair MH, Baker DA, Armstrong JD. Simple neurite tracer: open source software for reconstruction, visualization and analysis of neuronal processes. Bioinformatics. 2011; 27(17):2453-454.
76. Luczynski P, Whelan SO, O'sullivan C, Clarke G, Shanahan F, Dinan TG, Cryan JF. Adult microbiota-deficient mice have distinct dendritic morphological changes: differential effects in the amygdala and hippocampus. Eur J NeuroSci. 2016; 44(9):2654-666.
77. Li K, Zhong X, Yang S, Luo Z, Li K, Liu Y, Cai S, Gu H, Lu S, Zhang H, et al. Hipsc-derived retinal ganglion cells grow dendritic arbors and functional axons on a tissue-engineered scaffold. Acta Biomater. 2017; 54:117-27.
78. Valdez CM, Murphy GG, Beg AA. The rac-gap alpha2-chimaerin regulates hippocampal dendrite and spine morphogenesis. Mol Cell Neurosci. 2016; 75:14-26.
79. Raymond ES. The Cathedral & the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary. Sebastopol: "O'Reilly Media, Inc."; 2001.
80. ImageJ Communication Channels. https://imagej.net/Communication. Accessed 17 Jan 2017.
81. ImageJ Forum. http://forum.imagej.net/. Accessed 17 Jan 2017.
82. Discourse. https://www.discourse.org/. Accessed 17 Jan 2017.
83. SciJava Team Roles. https://imagej.net/Team. Accessed 17 Jan 2017.
84. ImageJ Roadmap. https://imagej.net/Roadmap. Accessed 17 Jan 2017.
85. Deep Learning. http://deeplearning.net/. Accessed 19 Jan 2017.
86. OpenCV: Open Source Computer Vision. http://opencv.org/. Accessed 17 Jan 2017.
87. Scikit-image: Image Processing in Python. http://scikit-image.org/. Accessed 17 Jan 2017.
88. Amazon Web Services. https://aws.amazon.com/. Accessed 17 Jan 2017.
89. Ferreira T. ImageJ User Guide. https://imagej.net/docs/guide/. Accessed 17 Jan 2017.
90. ImageJ 1.x Documentation. https://imagej.net/index.html. Accessed 17 Jan 2017.
91. ImageJ Information and Documentation Portal. http://imagejdocu.tudor.lu/. Accessed 17 Jan 2017.
92. ImageJ Search. https://search.imagej.net/. Accessed 17 Jan 2017.
93. ImageJ Funding. https://imagej.net/Funding. Accessed 17 Jan 2017.
94. Keating A. Bat Cochlea Volume. https://imagej.net/images/bat-cochlea-volume.txt. Accessed 17 Jan 2017.
95. Lorensen WE, Cline HE. Marching cubes: A high resolution 3d surface construction algorithm. In: ACM Siggraph Computer Graphics. vol. 21. New York: ACM: 1987. p. 163-9. doi: 10.1145/37401.37422.
96. Cignoni P, Callieri M, Corsini M, Dellepiane M, Ganovelli F, Ranzuglia G. Meshlab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference. vol. 2008. Salerno: The Eurographics Association: 2008. p. 129-36.
97. Dey N, Blanc-Feraud L, Zimmer C, Roux P, Kam Z, Olivo-Marin JC, Zerubia J. Richardson-lucy algorithm with total variation regularization for 3d confocal microscope deconvolution. Microsc Res Tech. 2006; 69(4):260-6.
98. McNamara G. Leica Microscope GPU Deconvolution Stellaris FISH Dataset #1. https://works.bepress.com/gmcnamara/31/. Accessed 17 Jan 2017.
99. Northan B. Flexible Deconvolution Using ImageJ Ops. https://imagej.github.io/presentations/2015-09-04-imagej2-deconvolution/. Accessed 17 Jan 2017.
100. Legland D, Arganda-Carreras I, Andrey P. Morpholibj: integrated library and plugins for mathematical morphology with imagej. Bioinformatics. 2016; 32(22):3532-534.
101. SciJava Scripting: Groovy. https://github.com/scijava/scripting-groovy. Accessed 8 Aug 2017.
102. BeanShell: Lightweight Scripting for Java. http://beanshell.org/. Accessed 8 Aug 2017.
103. SciJava Scripting: BeanShell. https://github.com/scijava/scripting-beanshell. Accessed 8 Aug 2017.
104. Scifio-bf-compat. https://github.com/scifio/scifio-bf-compat. Accessed 8 Aug 2017.
105. SCIFIO OME-XML Support. https://github.com/scifio/scifio-ome-xml. Accessed 8 Aug 2017.
106. Eclipse. https://eclipse.org/. Accessed 8 Aug 2017.
107. ImageJ Server. https://github.com/imagej/imagej-server. Accessed 8 Aug 2017.
108. ImageJ Legacy. https://github.com/imagej/imagej-legacy. Accessed 8 Aug 2017.
109. ImageJ 1.x Patcher. https://github.com/imagej/ij1-patcher. Accessed 8 Aug 2017.
110. SimpleITK. https://simpleitk.org/. Accessed 8 Aug 2017.
111. JavaScript. https://developer.mozilla.org/en-US/docs/Web/JavaScript. Accessed 8 Aug 2017.
112. SciJava Scripting: JavaScript. https://github.com/scijava/scripting-javascript. Accessed 8 Aug 2017.
113. Project Nashorn. http://openjdk.java.net/projects/nashorn/. Accessed 8 Aug 2017.
114. Rhino JavaScript Implementation. https://developer.mozilla.org/en-US/docs/Mozilla/Projects/Rhino. Accessed 8 Aug 2017.
115. Project Jupyter. https://jupyter.org/. Accessed 8 Aug 2017.
116. SciJava Jupyter Kernel. https://github.com/scijava/scijava-jupyter-kernel. Accessed 8 Aug 2017.
117. Beaker Extensions for Jupyter Notebook. https://github.com/twosigma/beakerx. Accessed 8 Aug 2017.
118. Kotlin. https://kotlinlang.org/. Accessed 8 Aug 2017.
119. SciJava Scripting: Kotlin. https://github.com/scijava/scripting-kotlin. Accessed 8 Aug 2017.
120. Lisp (programming Language). https://en.wikipedia.org/wiki/Lisp_(programming_language). Accessed 8 Aug 2017.
121. SciJava Scripting: Clojure. https://github.com/scijava/scripting-clojure. Accessed 8 Aug 2017.
122. The Clojure Programming Language. https://clojure.org/. Accessed 8 Aug 2017.
123. MATLAB: The Language of Technical Computing. https://www.mathworks.com/products/matlab.html. Accessed 8 Aug 2017.
124. SciJava Scripting: MATLAB. https://github.com/scijava/scripting-matlab. Accessed 8 Aug 2017.
125. Matlabcontrol. https://code.google.com/archive/p/matlabcontrol/. Accessed 8 Aug 2017.
126. Möller B, Glaß M, Misiak D, Posch S. Mitobo-a toolbox for image processing and analysis. J Open Res Softw. 2016; 4(1):17. doi: 10.5334/jors.103.
127. Alida. http://www.informatik.uni-halle.de/alida/. Accessed 8 Aug 2017.
128. Domínguez C, Heras J, Pascual V. Ij-opencv: Combining imagej and opencv for processing images in biomedicine. Comput Biol Med. 2017; 84(C):189-94.
129. JavaCV: Java Interface to OpenCV and More. https://github.com/bytedeco/javacv. Accessed 8 Aug 2017.
130. Python. https://python.org/. Accessed 8 Aug 2017.
131. Imglib, 2-imglyb. https://github.com/hanslovsky/imglib2-imglyb. Accessed 8 Aug 2017.
132. PyJNIus: Access Java Classes from Python. https://github.com/kivy/pyjnius. Accessed 8 Aug 2017.
133. Jython: Python for the Java Platform. http://jython.org/. Accessed 8 Aug 2017.
134. JyNI - Jython Native Interface. https://jyni.org/. Accessed 8 Aug 2017.
135. Imagey: ImageJ with CPython REPL. https://github.com/hanslovsky/imagey. Accessed 8 Aug 2017.
136. SciJava Scripting: CPython. https://github.com/scijava/scripting-cpython. Accessed 8 Aug 2017.
137. Python-javabridge: Python Wrapper for the Java Native Interface. https://github.com/LeeKamentsky/python-javabridge. Accessed 8 Aug 2017.
138. SciJava Scripting: Jython. https://github.com/scijava/scripting-jython. Accessed 8 Aug 2017.
139. The R Project for Statistical Computing. https://r-project.org/. Accessed 8 Aug 2017.
140. SciJava Scripting: Renjin. https://github.com/scijava/scripting-renjin. Accessed 8 Aug 2017.
141. Renjin. http://renjin.org/. Accessed 8 Aug 2017.
142. Representational State Transfer. https://en.wikipedia.org/wiki/Representational_state_transfer. Accessed 8 Aug 2017.
143. Dropwizard. http://dropwizard.io/. Accessed 8 Aug 2017.
144. Ruby Programming Language. https://www.ruby-lang.org/. Accessed 8 Aug 2017.
145. SciJava Scripting: JRuby. https://github.com/scijava/scripting-jruby. Accessed 8 Aug 2017.
146. The Scala Programming Language. https://scala-lang.org/. Accessed 8 Aug 2017.
147. SciJava Scripting: Scala. https://github.com/scijava/scripting-scala. Accessed 8 Aug 2017.
148. TensorFlow: An Open-source Software Library for Machine Intelligence. https://www.tensorflow.org/. Accessed 8 Aug 2017.
149. ImageJ-TensorFlow. https://github.com/imagej/imagej-tensorflow. Accessed 8 Aug 2017.
150. MaMuT. https://imagej.net/MaMuT. Accessed 14 Aug 2017.
151. Jug F, Pietzsch T, Kainmüller D, Funke J, Kaiser M, van Nimwegen E, Rother C, Myers G. Optimal joint segmentation and tracking of escherichia coli in the mother machine. In: Cardoso MJ, Simpson I, Arbel T, Precup D, Ribbens A, editors. Bayesian and graphical Models for Biomedical Imaging: First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers. Cham: Springer: 2014. p. 25-36. doi: 10.1007/978-3-319-12289-2_3.
152. Jug F, Pietzsch T, Kainmüller D, Myers G. Tracking by assignment facilitates data curation. In: MICCAI IMIC Workshop. vol. 2. Boston: Springer: 2014.
153. Mary H, Rueden C, Ferreira T. KymographBuilder: Release 1.2.4. 2016. doi: 10.5281/zenodo.56702.
154. Hanslovsky P, Bogovic JA, Saalfeld S. Post-acquisition image based compensation for thickness variation in microscopy section series. In: Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium On. New York: IEEE: 2015. p. 507-11. doi: 10.1109/ISBI.2015.7163922.
155. Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Seung HS. Trainable weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics. 2017; 33(15):2424-2426. doi: 10.1093/bioinformatics/btx180.
156. Daerr A, Mogne A. Pendent_drop: an imagej plugin to measure the surface tension from an image of a pendent drop. J Open Res Softw. 2016; 4(1):3. doi: 10.5334/jors.97.
157. SciView. https://github.com/scenerygraphics/SciView. Accessed 14 Aug 2017.
158. Preibisch S, Saalfeld S, Tomancak P. Globally optimal stitching of tiled 3d microscopic image acquisitions. Bioinformatics. 2009; 25(11):1463-1465.
159. Coloc 2. https://imagej.net/Coloc_2. Accessed 14 Aug 2017.