Source Code for Biology and Medicine | |
CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation | |
Arvid Lundervold2  Hans-Hermann Gerdes1  Dominik Michael Frei1  Tanja Kögel1  Erlend Hodneland1  | |
[1] Department of Biomedicine, University of Bergen, Bergen, Norway;Department of Radiology, Haukeland University Hospital, Bergen, Norway | |
关键词: Surface staining; Nucleus staining; High-throughput; CellSegm; Cell segmentation; Automated analysis; | |
Others : 805211 DOI : 10.1186/1751-0473-8-16 |
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received in 2012-11-27, accepted in 2013-07-30, 发布年份 2013 | |
【 摘 要 】
The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CELLSEGM, the software presented in this work, is a MATLAB based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CELLSEGM has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CELLSEGM. The command-line interface of CELLSEGM facilitates scripting of the separate tools, all implemented in MATLAB, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CELLSEGM enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.
【 授权许可】
2013 Hodneland et al.; licensee BioMed Central Ltd.
【 预 览 】
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【 参考文献 】
- [1]Sacan A, Ferhatosmanoglu H, Cosku H: CellTrack: an open-source software for cell tracking and motility analysis. Bioinformatics 2008, 24(14):1647-1649.
- [2]Tscherepanow M, Zöllner F, Kummert F: Automatic segmentation of unstained living cells in bright-field microscope images. In Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry (MDA 2008). Springer Lecture Notes in Computer Science Volume 5108; 2008:158-172.
- [3]Wu K, Gauthier D, Levine MD: Live cell image segmentation. IEEE Trans Biomed Eng 1995, 42:1-12.
- [4]Yu D, Pham TD, Yan H, Whang B, Crane DI: Segmentation of cultured neurons using logical analysis of grey and distance differences. J Neurosci methods 2007, 166:125-137. citeseer.ist.psu.edu/wu95live.html
- [5]Wählby C, Lindblad J, Vondrus M, Bengtsson E, Björkesten L: Algorithms for cytoplasm segmentation of fluorescence labelled cells. Anal Cell Pathol 2002, 24:101-111.
- [6]Wählby C, Sintorn IM, Erlandsson F, Borgefors G, Bengtsson E: Combining intensity,edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J Microsc 2004, 215:67-76.
- [7]Baggett D, Nakaya Ma, McAuliffe M, Yamaguchi TP, Lockett S: Whole cell segmentation in solid tissue sections. Cytometry Part A 2005, 67A:137-143.
- [8]Dow AI, Shafer SA, Kirkwood JM, Mascari RA, Waggoner AS: Automatic multiparameter fluorescence imaging for determining lymphocyte phenotype and activation status in melanoma tissue sections. Cytometry 1996, 25:71-81.
- [9]Solorzano COD, Malladi R, Lelièvre S, Lockett S: Segmentation of nuclei and cells using membrane related protein markers. J Microsc 2001, 201:404-415.
- [10]Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 2006, 7:1-11.
- [11]Gudla PR, Nandy K, Collins J, Meaburn KJ, Misteli T, Lockett SJ: "A high-throughput system for segmenting nuclei using multiscale techniques. Cytometry A 2008, 73(5):451-466.
- [12]Ambühl ME, Brepsant C, Meister JJ, Verkhovsky A, Sbalzarini I: High-resolution cell outline segmentation and tracking from phase-contrast microscopy images. J Microsc 2011, 245:161-170.
- [13]Ianzini F, Mackey M: Development of the large-scale digital cell analysis system. Radiat Prot Dosim 2002, 99:81-94.
- [14]Davis PJ, Kosmacek EA, Sun Y, Ianzini F, Mackay MA: The large-scale digital cell analysis system: an open system for nonperturbing live cell imaging. JMicrosc 2007, 228:296-308.
- [15]Han J, Chang H, Yang Q, Fontenay G, Groesser T, Barcellos-Hoff MH, Parvin B: Multiscale iterative voting for differential analysis of stress response for 2D and 3D cell culture models. J Microsc 2011, 241(3):315-326. http://www.biomedsearch.com/nih/Multiscale-iterative-voting-differential-analysis/21118235.html webcite
- [16]Adiga P: Integrated approach for segmentation of 3-D confocal images of a tissue specimen. Microsc Res Tech 2003, 54(4):260-270.
- [17]Adiga P, Chaudhuri B: Efficient cell segmentation tool for confocal microscopy tissue images and quantitative evaluation of FISH signals. Microsc Res Tech 1999, 44:49-68.
- [18]Li G, Liu T, Tarokh A, Nie J, Guo L, Mara A, Holley S, Wong S: 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biol 2007, 8(40):1-10.
- [19]Malpica N, Ortiz de Solórzano C, Vaquero J, Santos A, Vallcorba I, Francisco del P, Garcia-Sagredo J: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 1997, 28:289-297.
- [20]Weichert J: A review of nonlinear diffusion filtering. In Scale-Space Theory in Computer Vision. Lecture Notes in Computer Science, Vol. 1252, 3-28. Edited by Viergever M, Koenderink J, Florack L, ter Haar Romeny B, ter Haar Romeny B, Florack L, Koenderink J, Viergever M. Berlin: Springer; 1997.
- [21]Adiga U, Malladi R, Fernandez-Gonzalez R, Ortiz de Solorzano C: High-throughput analysis of multispectral images of breast cancer tissue. IEEE Trans Image Process 2006, 15(8):2259-2268.
- [22]Weickert J: Coherence-enhancing diffusion filtering. Int J Comput Vis 1999, 31(2-3):111-127.
- [23]Hodneland E, Bukoreshtliev NV, Eichler TW, Tai XC, Gurke S, Lundervold A, Gerdes HH: A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation. IEEE Trans Med Imaging 2009, 28(5):720-738. http://dx.doi.org/10.1109/TMI.2008.2011522 webcite
- [24]Vincent L, Soille P: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 1991, 13(6):583-598.