Aura-Net: Robust Segmentation Of Phase-Contrast Microscopy Images with Few Annotations

We present AURA-net, a convolutional neural network (CNN) for the segmentation of phase-contrast microscopy images. AURA-net uses transfer learning to accelerate training and Attention mechanisms to help the network focus on relevant image features. In this way, it can be trained efficiently with a very limited amount of annotations. Our network can thus be used to automate the segmentation of datasets that are generally considered too small for deep learning techniques. AURA-net also uses a loss inspired by active contours that is well-adapted to the specificity of phase-contrast images, further improving performance. We show that AURA-net outperforms state-of-the-art alternatives in several small (less than 100 images) datasets.

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

[2]  Amy Q. Shen,et al.  Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning , 2019, bioRxiv.

[3]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Peter J O'Toole,et al.  Characterising live cell behaviour: Traditional label-free and quantitative phase imaging approaches. , 2017, The international journal of biochemistry & cell biology.

[5]  Shenghua Gao,et al.  CE-Net: Context Encoder Network for 2D Medical Image Segmentation , 2019, IEEE Transactions on Medical Imaging.

[6]  Michael Unser,et al.  Snakes on a Plane: A perfect snap for bioimage analysis , 2015, IEEE Signal Processing Magazine.

[7]  Joe Chalfoun,et al.  Analyzing U-Net Robustness for Single Cell Nucleus Segmentation from Phase Contrast Images , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[8]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[9]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[10]  Bryan M. Williams,et al.  Learning Active Contour Models for Medical Image Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Robert A. Brown,et al.  Deep learning for high-throughput quantification of oligodendrocyte ensheathment at single-cell resolution , 2018, Communications Biology.

[12]  Keith E. Muller,et al.  Contrast-limited adaptive histogram equalization: speed and effectiveness , 1990, [1990] Proceedings of the First Conference on Visualization in Biomedical Computing.

[13]  E. Meijering.  A bird’s-eye view of deep learning in bioimage analysis , 2020, Computational and structural biotechnology journal.

[14]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[15]  F. Zernike.  Phase contrast, a new method for the microscopic observation of transparent objects , 1942 .

[16]  Loïc Le Folgoc,et al.  Attention U-Net: Learning Where to Look for the Pancreas , 2018, ArXiv.

[17]  Juho Kannala,et al.  Cell Segmentation Proposal Network for Microscopy Image Analysis , 2016, LABELS/DLMIA@MICCAI.

[18]  Margrit Betke,et al.  How to Collect Segmentations for Biomedical Images? A Benchmark Evaluating the Performance of Experts, Crowdsourced Non-experts, and Algorithms , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[19]  Thomas Brox,et al.  U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.

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

[21]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[22]  Vannary Meas-Yedid,et al.  Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing , 2002, IEEE Transactions on Medical Imaging.

[23]  Florian Jug,et al.  Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison , 2019, BMC Bioinformatics.