Efficient Oct Image Segmentation Using Neural Architecture Search

In this work, we propose a Neural Architecture Search (NAS) for retinal layer segmentation in Optical Coherence Tomography (OCT) scans. We incorporate the Unet architecture in the NAS framework as its backbone for the segmentation of the retinal layers in our collected and preprocessed OCT image dataset. At the pre-processing stage, we conduct super resolution and image processing techniques on the raw OCT scans to improve the quality of the raw images. For our search strategy, different primitive operations are suggested to find the down- & up-sampling cell blocks, and the binary gate method is applied to make the search strategy practical for the task in hand. We empirically evaluated our method on our in-house OCT dataset. The experimental results demonstrate that the self-adapting NAS-Unet architecture substantially outperformed the competitive human-designed architecture by achieving 95.4% in mean Intersection over Union metric and 78.7% in Dice similarity coefficient.

[1]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[2]  Alzheimer's Association∗.  2019 Alzheimer's disease facts and figures , 2019, Alzheimer's & Dementia.

[3]  T. Wong,et al.  Spectral-Domain OCT Measurements in Alzheimer's Disease: A Systematic Review and Meta-analysis. , 2019, Ophthalmology.

[4]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Dwarikanath Mahapatra,et al.  Image super-resolution using progressive generative adversarial networks for medical image analysis , 2019, Comput. Medical Imaging Graph..

[7]  Ning Xu,et al.  Wide Activation for Efficient and Accurate Image Super-Resolution , 2018, ArXiv.

[8]  Liang Lin,et al.  SNAS: Stochastic Neural Architecture Search , 2018, ICLR.

[9]  Yiming Yang,et al.  DARTS: Differentiable Architecture Search , 2018, ICLR.

[10]  Zhimin Xu,et al.  A Hierarchical Neural Network for Sequence-to-Sequences Learning , 2018, ArXiv.

[11]  Vijay Vasudevan,et al.  Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[13]  George Papandreou,et al.  Searching for Efficient Multi-Scale Architectures for Dense Image Prediction , 2018, NeurIPS.

[14]  J. Weuve,et al.  2016 Alzheimer's disease facts and figures , 2016 .

[15]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[16]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.

[17]  Yoshua Bengio,et al.  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[18]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Xiao Zeng,et al.  HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

[20]  Quoc V. Le,et al.  Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.

[21]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Dawn Xiaodong Song,et al.  Differentiable Neural Network Architecture Search , 2018, ICLR.

[23]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[24]  Ghassen Jerfel,et al.  AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles , 2019, ArXiv.

[25]  Li Fei-Fei,et al.  Progressive Neural Architecture Search , 2017, ECCV.

[26]  Qingquan Song,et al.  Auto-Keras: An Efficient Neural Architecture Search System , 2018, KDD.

[27]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[28]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[29]  Yujie Li,et al.  NAS-Unet: Neural Architecture Search for Medical Image Segmentation , 2019, IEEE Access.

[30]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[31]  Mirabela Rusu,et al.  An Application of Generative Adversarial Networks for Super Resolution Medical Imaging , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).