Abstract
Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods. Both suffer from crowded objects to varying degrees, merging adjacent objects or suppressing a valid object. In this work, we assign an embedding vector to each pixel through a deep neural network. The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd. Our method yields state-of-the-art results even with a light-weighted backbone network on a cell segmentation (BBBC006 + DSB2018) and a leaf segmentation data set (CVPPP2017). The code and model weights are public available (https://github.com/looooongChen/instance_segmentation_with_pixel_embeddings/).
This work was supported by the Deutsche Forschungsgemeinschaft (Research Training Group 2416 MultiSenses-MultiScales).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Scharr, H., et al.: Leaf segmentation in plant phenotyping: a collation study. Mach. Vis. Appl. 27(4), 585–606 (2016)
Ulman, V., et al.: An objective comparison of cell-tracking algorithms. Nat. Methods 14, 1141–1152 (2017)
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_28
Chen, H., Qi, X., Yu L., Dou, Q., Qin, J., Heng, P.A.: DCAN: deep contour-aware networks for accurate gland segmentation. In: 2016 CVPR, pp. 2487–2496 (2016)
Ren, S., He, K, Girshick, R., Sun, J,: Faster R-CNN: towards real-time object detection with region proposal networks. In: 28th NIPS, pp. 91–99 (2015)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: 2017 ICCV, pp. 2980-2988 (2017)
Schmidt, U., Weigert, M., Broaddus, C., Myers, G.: Cell detection with star-convex polygons. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 265–273. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_30
Jetley, S., Sapienza, M., Golodetz, S., Torr, P.H.: Straight to shapes: real-time detection of encoded shapes. In: 2017 CVPR, pp. 4207–4216 (2017)
De Brabandere, B., Neven, D., Van Gool, L.: Semantic instance segmentation with a discriminative loss function. CoRR (2017)
Fathi, A., et al.: Semantic instance segmentation via deep metric learning. CoRR (2017)
De Brabandere, B., Neven, D., Van Gool, L.: Semantic instance segmentation for autonomous driving. In: 2017 CVPR Workshop, pp. 478–480 (2017)
Kong, S., Fowlkes, C.C.: Recurrent pixel embedding for instance grouping. In: CVPR, pp. 9018–9028 (2018)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 CVPR, pp. 770–778 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, L., Strauch, M., Merhof, D. (2019). Instance Segmentation of Biomedical Images with an Object-Aware Embedding Learned with Local Constraints. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-32239-7_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32238-0
Online ISBN: 978-3-030-32239-7
eBook Packages: Computer ScienceComputer Science (R0)
