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Active Learning and Proofreading for Delineation of Curvilinear Structures

  • Agata Mosinska
  • Jakub Tarnawski
  • Pascal Fua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

Many state-of-the-art delineation methods rely on supervised machine learning algorithms. As a result, they require manually annotated training data, which is tedious to obtain. Furthermore, even minor classification errors may significantly affect the topology of the final result. In this paper we propose a generic approach to addressing both of these problems by taking into account the influence of a potential misclassification on the resulting delineation. In an Active Learning context, we identify parts of linear structures that should be annotated first in order to train a classifier effectively. In a proofreading context, we similarly find regions of the resulting reconstruction that should be verified in priority to obtain a nearly-perfect result. In both cases, by focusing the attention of the human expert on potential classification mistakes which are the most critical parts of the delineation, we reduce the amount of required supervision. We demonstrate the effectiveness of our approach on microscopy images depicting blood vessels and neurons.

Keywords

Active Learning Proofreading Delineation Light microscopy Mixed integer programming 

References

  1. 1.
    Ascoli, G., Svoboda, K., Liu, Y.: Digital reconstruction of axonal and dendritic morphology DIADEM challenge (2010). http://diademchallenge.org/
  2. 2.
    Becker, C., Rigamonti, R., Lepetit, V., Fua, P.: Supervised feature learning for curvilinear structure segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 526–533. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40811-3_66 CrossRefGoogle Scholar
  3. 3.
    Breitenreicher, D., Sofka, M., Britzen, S., Zhou, S.K.: Hierarchical discriminative framework for detecting tubular structures in 3D images. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 328–339. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38868-2_28 CrossRefGoogle Scholar
  4. 4.
    Dercksen, V., Hege, H., Oberlaender, M.: The filament editor: an interactive software environment for visualization, proof-editing and analysis of 3D neuron morphology. Neuroinformatics 12, 325–339 (2014)CrossRefGoogle Scholar
  5. 5.
    Freytag, A., Rodner, E., Denzler, J.: Selecting influential examples: active learning with expected model output changes. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 562–577. Springer, Cham (2014). doi: 10.1007/978-3-319-10593-2_37 Google Scholar
  6. 6.
    González, G., Fleuret, F., Fua, P.: Automated delineation of dendritic networks in noisy image stacks. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 214–227. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88693-8_16 CrossRefGoogle Scholar
  7. 7.
    Law, M.W.K., Chung, A.C.S.: Three dimensional curvilinear structure detection using optimally oriented flux. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 368–382. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88693-8_27 CrossRefGoogle Scholar
  8. 8.
    Mosinska, A., Sznitman, R., Glowacki, P., Fua, P.: Active learning for delineation of curvilinear structures. In: CVPR (2016)Google Scholar
  9. 9.
    Neher, P.F., Götz, M., Norajitra, T., Weber, C., Maier-Hein, K.H.: A machine learning based approach to fiber tractography using classifier voting. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 45–52. Springer, Cham (2015). doi: 10.1007/978-3-319-24553-9_6 CrossRefGoogle Scholar
  10. 10.
    Peng, H., Long, F., Myers, G.: Automatic 3D neuron tracing using all-path pruning. Bioinformatics 27(13), 239–247 (2011)CrossRefGoogle Scholar
  11. 11.
    Peng, H., Long, F., Zhao, T., Myers, E.: Proof-editing is the bottleneck of 3D neuron reconstruction: the problem and solutions. Neuroinformatics 9(2), 103–105 (2011)CrossRefGoogle Scholar
  12. 12.
    Santamaría-Pang, A., Hernandez-Herrera, P., Papadakis, M., Saggau, P., Kakadiaris, I.: Automatic morphological reconstruction of neurons from multiphoton and confocal microscopy images using 3D tubular models. Neuroinformatics 13(3), 1–24 (2015)CrossRefGoogle Scholar
  13. 13.
    Settles, B.: Active learning literature survey. Technical report, University of Wisconsin-Madison (2010)Google Scholar
  14. 14.
    Sironi, A., Turetken, E., Lepetit, V., Fua, P.: Multiscale centerline detection. PAMI 38, 1327–1341 (2016)CrossRefGoogle Scholar
  15. 15.
    Turetken, E., Becker, C., Glowacki, P., Benmansour, F., Fua, P.: Detecting irregular curvilinear structures in gray scale and color imagery using multi-directional oriented flux. In: ICCV, December 2013Google Scholar
  16. 16.
    Turetken, E., Benmansour, F., Andres, B., Glowacki, P., Pfister, H., Fua, P.: Reconstructing curvilinear networks using path classifiers and integer programming. PAMI 38, 2515–2530 (2016)CrossRefGoogle Scholar
  17. 17.
    Montoya-Zegarra, J.A., Wegner, J.D., Ladický, Ľ., Schindler, K.: Mind the gap: modeling local and global context in (road) networks. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 212–223. Springer, Cham (2014). doi: 10.1007/978-3-319-11752-2_17 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.École Polytechnique Fédérale de LausanneLausanneSwitzerland

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