LayeredCNN: Segmenting Layers with Autoregressive Models
DOI:
https://doi.org/10.7557/18.6254Keywords:
Segmentation, Deep Learning, Autoregressive, Convolutional Neural NetworkAbstract
We address a subclass of segmentation problems where the labels of the image are structured in layers. We propose applying autoregressive CNNs which, when given an image and a partial segmentation of layers, complete the segmentation. Initializing the model with a user-provided partial segmentation allows for choosing which layers the model should segment. Alternatively, the model can produce an automatic initialization, albeit with some performance loss. The model is trained exclusively on synthetic data from our data generation algorithm. It yields impressive performance on the synthetic data and generalizes to real data it has never seen.
References
J. Beutel, H. L. Kundel, and R. L. Van Metter. Handbook of medical imaging, volume 1. Spie Press, 2000.
Dong-Chen He and Abdelmounaime Safia. Brodatz’s texture database. http://multibandtexture.recherche.usherbrooke.ca/original_brodatz.html
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001.
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. CVPR, 2016. doi.org/10.1109/CVPR.2016.90
L. Hubert and P. Arabie. Comparing partitions. Journal of classification, 2(1):193–218, 1985. doi.org/10.1007/BF01908075
S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. ICML, 2015. https://arxiv.org/abs/1502.03167
N. Jeppesen, A. N. Christensen, V. A. Dahl, and A. B. Dahl. Sparse layered graphs for multi-object segmentation. CVPR, 2020. doi.org/10.1109/CVPR42600.2020.01279
D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. ICLR, 2015. https://arxiv.org/abs/1412.6980
H. A. Leopold, J. Orchard, J. S. Zelek, and V. Lakshminarayanan. Pixelbnn: Augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation. Journal of Imaging, 5, 2019. doi.org/10.3390/jimaging5020026
K. Li, X. Wu, D. Chen, and M. Sonka. Optimal surface segmentation in volumetric images - a graph-theoretic approach. PAMI, 28(1):119–134, 2006. doi.org/10.1109/TPAMI.2006.19
Paul Mooney. Retinal OCT Images (optical coherence tomography). Kaggle dataset, 2018. https://www.kaggle.com/paultimothymooney/kermany2018
O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. MICCAI, 2015. doi.org/10.1007/978-3-319-24574-4_28
T. Salimans, A. Karpathy, X. Chen, and D. P.Kingma. Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications. ICLR, 2017. http://arxiv.org/abs/1701.05517
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu. Pixel recurrent neural net-works. ICML, 2016. http://arxiv.org/abs/1601.06759
A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, and K. Kavukcuoglu. Conditional image generation with pixelcnn decoders. NIPS, 2016. http://arxiv.org/abs/1606.05328
X. Wu and D. Z. Chen. Optimal net surface problems with applications. ICALP, 2002. doi.org/10.1007/3-540-45465-9_88
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Jakob L. Christensen, Patrick Møller Jensen, Morten Rieger Hannemose, Anders Bjorholm Dahl, Vedrana Andersen Dahl
This work is licensed under a Creative Commons Attribution 4.0 International License.
