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2020 | OriginalPaper | Buchkapitel

PatchPerPix for Instance Segmentation

verfasst von : Lisa Mais, Peter Hirsch, Dagmar Kainmueller

Erschienen in: Computer Vision – ECCV 2020

Verlag: Springer International Publishing

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Abstract

We present a novel method for proposal free instance segmentation that can handle sophisticated object shapes which span large parts of an image and form dense object clusters with crossovers. Our method is based on predicting dense local shape descriptors, which we assemble to form instances. All instances are assembled simultaneously in one go. To our knowledge, our method is the first non-iterative method that yields instances that are composed of learnt shape patches. We evaluate our method on a diverse range of data domains, where it defines the new state of the art on four benchmarks, namely the ISBI 2012 EM segmentation benchmark, the BBBC010 C. elegans dataset, and 2d as well as 3d fluorescence microscopy data of cell nuclei. We show furthermore that our method also applies to 3d light microscopy data of Drosophila neurons, which exhibit extreme cases of complex shape clusters.

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Vorheriges Kapitel Fixing Localization Errors to Improve Image Classification

Nächstes Kapitel Attend and Segment: Attention Guided Active Semantic Segmentation

Nur mit Berechtigung zugänglich

BBBC010v1: C.elegans infection live/dead image set version 1 provided by Fred Ausubel.

https://www.kaggle.com/c/data-science-bowl-2018.

http://brainiac2.mit.edu/isbi_challenge/leaders-board-new.

BBBC038v1: available from the Broad Bioimage Benchmark Collection [21].

Arganda-Carreras, I., et al.: Crowdsourcing the creation of image segmentation algorithms for connectomics. Front. Neuroanat. 9, 142 (2015)CrossRef

Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)CrossRef

Bai, M., Urtasun, R.: Deep watershed transform for instance segmentation. CoRR abs/1611.08303 (2016)

Chen, L., Strauch, M., Merhof, D.: Instance segmentation of biomedical images with an object-aware embedding learned with local constraints. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 451–459. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_50CrossRef

Chen, X., Girshick, R.B., He, K., Dollár, P.: TensorMask: a foundation for dense object segmentation. CoRR abs/1903.12174 (2019). http://arxiv.org/abs/1903.12174

Dai, J., He, K., Li, Y., Ren, S., Sun, J.: Instance-sensitive fully convolutional networks. CoRR abs/1603.08678 (2016), http://arxiv.org/abs/1603.08678

De Brabandere, B., Neven, D., Van Gool, L.: Semantic instance segmentation with a discriminative loss function. arXiv preprint arXiv:1708.02551 (2017)

Funke, J., et al.: Large scale image segmentation with structured loss based deep learning for connectome reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. (2018). https://doi.org/10.1109/TPAMI.2018.2835450

Gao, N., et al.: SSAP: single-shot instance segmentation with affinity pyramid. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 642–651 (2019)

10.

Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2014, pp. 580–587. IEEE Computer Society, Washington, DC (2014). https://doi.org/10.1109/CVPR.2014.81

11.

He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2017). http://arxiv.org/abs/1703.06870. Comment: open source; appendix on more results

12.

Hirsch, P., Kainmueller, D.: An auxiliary task for learning nuclei segmentation in 3d microscopy images. In: Medical Imaging with Deep Learning (MIDL), July 2020

13.

Januszewski, M., Maitin-Shepard, J., Li, P., Kornfeld, J., Denk, W., Jain, V.: Flood-filling networks. CoRR abs/1611.00421 (2016). http://arxiv.org/abs/1611.00421

14.

Jenett, A., et al.: A gal4-driver line resource for drosophila neurobiology. Cell reports 2(4), 991–1001 (2012). https://doi.org/10.1016/j.celrep.2012.09.011, https://www.ncbi.nlm.nih.gov/pubmed/23063364, 23063364[pmid]

15.

Jetley, S., Sapienza, M., Golodetz, S., Torr, P.H.S.: Straight to shapes: real-time detection of encoded shapes. CoRR abs/1611.07932 (2016), http://arxiv.org/abs/1611.07932

16.

Keuper, M., Levinkov, E., Bonneel, N., Lavoué, G., Brox, T., Andres, B.: Efficient decomposition of image and mesh graphs by lifted multicuts. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1751–1759 (2015)

17.

Kulikov, V., Lempitsky, V.: Instance segmentation of biological images using harmonic embeddings. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020

18.

Lee, K., Lu, R., Luther, K., Seung, H.S.: Learning dense voxel embeddings for 3D neuron reconstruction (2019)

19.

Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48CrossRef

20.

Liu, Y., et al.: Affinity derivation and graph merge for instance segmentation. CoRR abs/1811.10870 (2018). http://arxiv.org/abs/1811.10870

21.

Ljosa, V., Sokolnicki, K.L., Carpenter, A.E.: Annotated high-throughput microscopy image sets for validation. Nat. Methods 9, 637 EP (2012). https://doi.org/10.1038/nmeth.2083

22.

Long, F., Peng, H., Liu, X., Kim, S.K., Myers, E.: A 3D digital atlas of C. elegans and its application to single-cell analyses. Nat. Methods 6(9), 667–672 (2009). https://doi.org/10.1038/nmeth.1366

23.

Maitin-Shepard, J.B., Jain, V., Januszewski, M., Li, P., Abbeel, P.: Combinatorial energy learning for image segmentation. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 1966–1974. Curran Associates, Inc. (2016). http://papers.nips.cc/paper/6595-combinatorial-energy-learning-for-image-segmentation.pdf

24.

Nern, A., Pfeiffer, B.D., Rubin, G.M.: Optimized tools for multicolor stochastic labeling reveal diverse stereotyped cell arrangements in the fly visual system. Proc. Natl. Acad. Sci. 112(22), E2967–E2976 (2015). https://doi.org/10.1073/pnas.1506763112, https://www.pnas.org/content/112/22/E2967

25.

Novotny, D., Albanie, S., Larlus, D., Vedaldi, A.: Semi-convolutional operators for instance segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 89–105. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_6CrossRef

26.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRef

27.

Schmidt, U., Weigert, M., Broaddus, C., Myers, G.: Cell detection with star-convex polygons. CoRR abs/1806.03535 (2018)

28.

Wählby, C., et al.: An image analysis toolbox for high-throughput c. elegans assays. Nat. Methods 9(7), 714–716 (2012). https://doi.org/10.1038/nmeth.1984

29.

Weigert, M., Schmidt, U., Haase, R., Sugawara, K., Myers, G.: Star-convex polyhedra for 3D object detection and segmentation in microscopy. arXiv:1908.03636 (2019)

30.

Wolf, S., et al.: The mutex watershed: efficient, parameter-free image partitioning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 571–587. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_34CrossRef

31.

Wolf, S., Schott, L., Kothe, U., Hamprecht, F.: Learned watershed: end-to-end learning of seeded segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2011–2019 (2017)

32.

Yurchenko, V., Lempitsky, V.S.: Parsing images of overlapping organisms with deep singling-out networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 4752–4760 (2017). https://doi.org/10.1109/CVPR.2017.505

Titel: PatchPerPix for Instance Segmentation
verfasst von: Lisa Mais
Peter Hirsch
Dagmar Kainmueller
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2020
Print ISBN: 978-3-030-58594-5

Electronic ISBN: 978-3-030-58595-2

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-58595-2_18

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