Evaluation of the effectiveness of simple nuclei-segmentation methods on Caenorhabditis elegans embryogenesis images

BMC Bioinformatics

Yusuke Azuma¹  Shuichi Onami² 

[1] Laboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan

[2] National Bioscience Database Center, Japan Science and Technology Agency, 5-3 Yonbancho, Chiyoda-ku, Tokyo 102-0081, Japan

关键词: Embryonic development;    Caenorhabditis elegans;    Crowded nuclei;    Image processing;    Nuclei segmentation;    Bioimage informatics;   

Others  :  1087741
DOI  :  10.1186/1471-2105-14-295

 received in 2013-03-15, accepted in 2013-07-15,  发布年份 2013

【 摘 要 】

Background

For the analysis of spatio-temporal dynamics, various automated processing methods have been developed for nuclei segmentation. These methods tend to be complex for segmentation of images with crowded nuclei, preventing the simple reapplication of the methods to other problems. Thus, it is useful to evaluate the ability of simple methods to segment images with various degrees of crowded nuclei.

Results

Here, we selected six simple methods from various watershed based and local maxima detection based methods that are frequently used for nuclei segmentation, and evaluated their segmentation accuracy for each developmental stage of the Caenorhabditis elegans. We included a 4D noise filter, in addition to 2D and 3D noise filters, as a pre-processing step to evaluate the potential of simple methods as widely as possible. By applying the methods to image data between the 50- to 500-cell developmental stages at 50-cell intervals, the error rate for nuclei detection could be reduced to ≤ 2.1% at every stage until the 350-cell stage. The fractions of total errors throughout the stages could be reduced to ≤ 2.4%. The error rates improved at most of the stages and the total errors improved when a 4D noise filter was used. The methods with the least errors were two watershed-based methods with 4D noise filters. For all the other methods, the error rate and the fraction of errors could be reduced to ≤ 4.2% and ≤ 4.1%, respectively. The minimum error rate for each stage between the 400- to 500-cell stages ranged from 6.0% to 8.4%. However, similarities between the computational and manual segmentations measured by volume overlap and Hausdorff distance were not good. The methods were also applied to Drosophila and zebrafish embryos and found to be effective.

Conclusions

The simple segmentation methods were found to be useful for detecting nuclei until the 350-cell stage, but not very useful after the 400-cell stage. The incorporation of a 4D noise filter to the simple methods could improve their performances. Error types and the temporal biases of errors were dependent on the methods used. Combining multiple simple methods could also give good segmentations.

【 授权许可】

   
2013 Azuma and Onami; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Keller PJ, Stelzer EHK: Digital scanned laser light sheet fluorescence microscopy. Cold Spring Harb Protoc 2010., 2010(5) pdb.top78
• [2]Cho BH, Cao-Berg I, Bakal JA, Murphy RF: OMERO.searcher: content-based image search for microscope images. Nat Methods 2012, 9:633-634.
• [3]Long F, Zhou J, Peng H: Visualization and analysis of 3D microscopic images. PLoS Comput Biol 2012, 8:e1002519.
• [4]Bao Z, Murray JI, Boyle T, Ooi SL, Sandel MJ, Waterston RH: Automated cell lineage tracing in Caenorhabditis elegans. Proc Natl Acad Sci USA 2006, 103:2707-2712.
• [5]McMahon A, Supatto W, Fraser S, Stathopoulos A: Dynamic analyses of Drosophila gastrulation provide insights into collective cell migration. Sci 2008, 322:1546-1550.
• [6]Keller PJ, Schmidt AD, Wittbrodt J, Stelzer EHK: Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Sci 2008, 322:1065-1069.
• [7]Meijering E, Dzyubachyk O, Smal I, Van Cappellen WA: Tracking in cell and developmental biology. Semin Cell Dev Biol 2009, 20:894-902.
• [8]Meijering E, Dzyubachyk O, Smal I: Chapter nine – methods for cell and particle tracking. Methods Enzymol 2012, 504:183-200.
• [9]Luengo-Oroz MA, Ledesma-Carbayo MJ, Peyriéras N, Santos A: Image analysis for understanding embryo development: a bridge from microscopy to biological insights. Curr Opin Genet Dev 2011, 21:630-637.
• [10]Long F, Peng H, Myers E: Automatic Segmentation of Nuclei in 3D Microscopy Images of C. Elegans. IEEE Int Symp Biomed Imaging 2007, 536-539.
• [11]Xiao H, Li Y, Du J, Mosig A: Ct3d: tracking microglia motility in 3D using a novel cosegmentation approach. Bioinformatics 2011, 27:564-571.
• [12]Chen S, Zhao M, Wu G, Yao C, Zhang J: Recent advances in morphological cell image analysis. Comput Math Methods Med 2012, 2012:101536.
• [13]Malpica N, De Solórzano CO, Vaquero JJ, Santos A, Vallcorba I, García-Sagredo JM, Del Pozo F: Applying watershed algorithms to the segmentation of clustered nuclei. Cytom 1997, 28:289-297.
• [14]Santella A, Du Z, Nowotschin S, Hadjantonakis A-K, Bao Z: A hybrid blob-slice model for accurate and efficient detection of fluorescence labeled nuclei in 3D. BMC Bioinforma 2010, 11:580. BioMed Central Full Text
• [15]Shariff A, Kangas J, Coelho LP, Quinn S, Murphy RF: Automated image analysis for high-content screening and analysis. J Biomol Screen 2010, 15:726-34.
• [16]Li G, Liu T, Tarokh A, Nie J, Guo L, Mara A, Holley S, Wong STC: 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biol 2007, 8:40. BioMed Central Full Text
• [17]Wang M, Zhou X, Li F, Huckins J, King RW, Wong STC: Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy. Bioinformatics 2008, 24:94-101.
• [18]Lin G, Adiga U, Olson K, Guzowski JF, Barnes CA, Roysam B: A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry A 2003, 56:23-36.
• [19]Bieniecki W: Oversegmentation avoidance in watershed-based algorithms for color images. Modern Problems of Radio Engineering, Telecommunications and Computer Science, 2004. Proceedings of the International Conference 2004. 2004, 169-172.
• [20]Keller PJ, Schmidt AD, Santella A, Khairy K, Bao Z, Wittbrodt J, Stelzer EHK: Fast, high-contrast imaging of animal development with scanned light sheet-based structured-illumination microscopy. Nat Methods 2010, 7:637-42.
• [21]Kosior JC, Kosior RK, Frayne R: Robust dynamic susceptibility contrast MR perfusion using 4D nonlinear noise filters. J Magn Reson Imaging 2007, 26:1514-22.
• [22]Mendrik AM, Vonken E, Van Ginneken B, De Jong HW, Riordan A, Van Seeters T, Smit EJ, Viergever MA, Prokop M: TIPS bilateral noise reduction in 4D CT perfusion scans produces high-quality cerebral blood flow maps. Phys Med Biol 2011, 56:3857-3872.
• [23]Huttenlocher DP, Klanderman GA, Rucklidge WJ: Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell 1993, 15:850-863.
• [24]Murray J, Boyle T, Preston E: Multidimensional regulation of gene expression in the C. elegans embryo. Genome Res 2012, 22:1282-1294.
• [25]Nance J, Lee J-Y, Goldstein B: Gastrulation in C. elegans. Worm Book 2005, 1-13.
• [26]Jaensch S, Decker M, Hyman AA, Myers EW: Automated tracking and analysis of centrosomes in early Caenorhabditis elegans embryos. Bioinformatics 2010, 26:i13-20.
• [27]Neumann B, Walter T, Hériché J-K, Bulkescher J, Erfle H, Conrad C, Rogers P, Poser I, Held M, Liebel U, Cetin C, Sieckmann F, Pau G, Kabbe R, Wünsche A, Satagopam V, Schmitz MHA, Chapuis C, Gerlich DW, Schneider R, Eils R, Huber W, Peters J-M, Hyman AA, Durbin R, Pepperkok R, Ellenberg J: Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nat 2010, 464:721-727.
• [28]Lindblad J, Bengtsson E: A comparison of methods for estimation of intensity non uniformities in 2D and 3D microscope images of fluorescence stained cells. Proc 12th Scandinavian Conference on Image Analysis (SCIA) 2001, 264-271.
• [29]Fernandez R, Das P, Mirabet V, Moscardi E, Traas J, Verdeil J-L, Malandain G, Godin C: Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution. Nat Meth 2010, 7:547-553.