Download PDF
Methods Inf Med 2012; 51(03): 260-267
DOI: 10.3414/ME11-02-0015
Focus Theme – Original Articles
Schattauer GmbH

A Fast, Automatic Segmentation Algorithm for Locating and Delineating Touching Cell Boundaries in Imaged Histopathology

X. Qi
1   Department of Pathology and Laboratory Medicine, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NJ, USA
2   Center for Biomedical Imaging & Informatics, The Cancer Institute of New Jersey, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NJ, USA
,
F. Xing
3   Division of Biomedical Informatics, Department of Biostatistics, University of Kentucky, Lexington, KY, USA
,
D. J. Foran
1   Department of Pathology and Laboratory Medicine, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NJ, USA
2   Center for Biomedical Imaging & Informatics, The Cancer Institute of New Jersey, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NJ, USA
,
L. Yang
2   Center for Biomedical Imaging & Informatics, The Cancer Institute of New Jersey, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NJ, USA
3   Division of Biomedical Informatics, Department of Biostatistics, University of Kentucky, Lexington, KY, USA
› Author Affiliations
Further Information

Publication History

received:24 February 2011

accepted:13 February 2012

Publication Date:
20 January 2018 (online)

    Permissions and Reprints

Summary

Background: Automated analysis of imaged histopathology specimens could potentially provide support for improved reliability in detection and classification in a range of investigative and clinical cancer applications. Automated segmentation of cells in the digitized tissue microarray (TMA) is often the prerequisite for quantitative analysis. However overlapping cells usually bring significant challenges for traditional segmentation algorithms.

Objectives: In this paper, we propose a novel, automatic algorithm to separate overlapping cells in stained histology specimens acquired using bright-field RGB imaging.

Methods: It starts by systematically identifying salient regions of interest throughout the image based upon their underlying visual content. The segmentation algorithm subsequently performs a quick, voting based seed detection. Finally, the contour of each cell is obtained using a repulsive level set deformable model using the seeds generated in the previous step. We compared the experimental results with the most current literature, and the pixel wise accuracy between human experts’ annotation and those generated using the automatic segmentation algorithm.

Results: The method is tested with 100 image patches which contain more than 1000 overlapping cells. The overall precision and recall of the developed algorithm is 90% and 78%, respectively. We also implement the algorithm on GPU. The parallel implementation is 22 times faster than its C/C++ sequential implementation.

Conclusion: The proposed segmentation algorithm can accurately detect and effectively separate each of the overlapping cells. GPU is proven to be an efficient parallel platform for overlapping cell segmentation.

Keywords

Parallel computing - segmentation - seed detection - pathology images
 

  • References

  • 1 Jemal A, Siegel R, Xu J, Ward E. Cancer Statistics, 2010. CA Cancer J Clin 2010
    Google Scholar
  • 2 Rimm DL, Camp RL, Charette LA, Costa J, Olsen DA, Reiss M. Tissue microarray: a new technology for amplification of tissue resources. Cancer Journal 2001; 7 (01) 24-31.
    PubMedGoogle Scholar
  • 3 Bartels PH, Gahm T, Thompson D. Automated microscopy in diagnostic histopathology: from image processing to automated reasoning. International Journal of Image Systems and Technology 1997; 8: 214-223.
    PubMedGoogle Scholar
  • 4 Datar M, Padfield D, Cline H. Color and texture based segmentation of molecular pathology images using hsoms. Proceedings of IEEE International Symposium on Biomedical Imaging. 2008: 292-295.
    Google Scholar
  • 5 Amaral T, McKenna S, Robertson K, Thompson A. Classification of breast-tissue microarry spots using colour and local invariants. Proceedings of IEEE International Symposium on Biomedical Imaging. 2008: 999-1002.
    Google Scholar
  • 6 Hafiane A, Bunyak F, Palaniappan K. Evaluation of level set-based histology image segmentation using geometric region criteria. Proceedings of IEEE International Symposium on Biomedical Imaging. 2009. MP-PA1 1-4.
    Google Scholar
  • 7 Wahlby C, Lindblad J, Vondrus M, Bengtsson E, Bjorkesten L. Algorithms for cytoplasm segmentation of fluorescence labelled cells. Anal Cell Pathol 2002; 24: 101-111.
    PubMedGoogle Scholar
  • 8 Zhou X, Liu KY, Bradblad P, Perrimon N, Wang STC. Towards automated cellular image segmentation for RNAi genome-wide screening. Proceeding of Medical Image Computing and Computer Assisted Intervention 2005; 3749: 885-892.
    PubMedGoogle Scholar
  • 9 Wen Q, Chang H, Parvin B. A delaunary triangulation approach for segmenting clumps on nuclei. Proceedings of IEEE International Symposium on Biomedical Imaging. 2009: 9-12.
    Google Scholar
  • 10 Kothari S, Chaudry Q, Wang WD. Automated cell counting and cluster segmentation using convavity detection and ellipse fitting techniques. Proceedings of IEEE International Symposium on Biomedical Imaging 2009; 2: 795-798.
    PubMedGoogle Scholar
  • 11 Yang L, Tuzel O, Meer P, Foran DJ. Automatic image analysis of histopathology specimens using concave vertex graph. International Conference on Medical Image Computing and Computer Assisted Intervention 2008; 5241: 833-841.
    PubMedGoogle Scholar
  • 12 Faustino GM, Gattass M, Rehen S, Lucena CJP. Automatic embryonic stem cells detection and counting method in fluorescence microscopy images. Proceedings of IEEE International Symposium on Biomedical Imaging. 2009: 799-802.
    Google Scholar
  • 13 Wittenberg T, Grobe M, Münzenmayer C, Kuziela H, Spinnler K. A semantic approach to segmentation of overlapping objects. Methods Inf Med 2004; 43 (04) 343-353.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 14 Parvin B, Yang Q, Han J, Chang H, Rydberg B, Barcellos-Hoff MH. Iterative voting for inference of structural saliency and characterization of subcellular events. IEEE Transactions on Image Processing 2007; 16 (03) 615-623.
    CrossrefPubMedGoogle Scholar
  • 15 Chan TF, Vese LA. Active contours without edges. IEEE Transaction of Image Processing 2001; 10 (02) 266-277.
    PubMedGoogle Scholar
  • 16 Yan P, Zhou X, Shah M, Wong STC. Automatic segmentation of high-throughput RNAi fluorescent cellular images. IEEE Transactions on Information Technology in Biomedicine 2008; 12 (01) 109-117.
    CrossrefPubMedGoogle Scholar
  • 17 Grady L, Schwartz EL. Isoperimetric graph partitioning for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006; 28 (01) 469-475.
    CrossrefPubMedGoogle Scholar
  • 18 Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Transactions on Biomedical Engineering 2010; 57 (04) 841-852.
    CrossrefPubMedGoogle Scholar