Extracting Retinal Anatomy and Pathological Structure Using Multiscale Segmentation

Fundus image segmentation technology has always been an important tool in the medical imaging field. Recent studies have validated that deep learning techniques can effectively segment retinal anatomy and determine pathological structure in retinal fundus photographs. However, several groups of image segmentation methods used in medical imaging only provide a single retinopathic feature (e.g., roth spots and exudates). In this paper, we propose a more accurate and clinically oriented framework for the segmentation of fundus images from end-to-end input. We design a four-path multiscale input network structure that learns network features and finds overall characteristics via our network. Our network’s structure is not limited by segmentation of single retinopathic features. Our method is suitable for exudates, roth spots, blood vessels, and optic discs segmentation. The structure has general applicability to many fundus models; therefore, we use our own dataset for training. In cooperation with hospitals and board-certified ophthalmologists, the proposed framework is validated on retinal images from large databases and can improve diagnostic performance compared to state-of-the-art methods that use smaller databases for training. The proposed framework detects blood vessels with an accuracy of 0.927, which is comparable to exudate accuracy (0.939) and roth spot accuracy (0.904), providing ophthalmologists with a practical diagnostic and a robust analytical tool.

[1]  Guy Cazuguel,et al.  TeleOphta: Machine learning and image processing methods for teleophthalmology , 2013 .

[2]  Ke Chen,et al.  Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images , 2015, IEEE Transactions on Medical Imaging.

[3]  G. Quellec,et al.  Automated analysis of retinal images for detection of referable diabetic retinopathy. , 2013, JAMA ophthalmology.

[4]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[5]  Huazhu Fu,et al.  Retinal vessel segmentation via deep learning network and fully-connected conditional random fields , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[6]  Marcin Ciecholewski,et al.  Semi-Automatic Corpus Callosum Segmentation and 3D Visualization Using Active Contour Methods , 2018, Symmetry.

[7]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Zhenfeng Zhang,et al.  Superpixel-Based Segmentation for 3D Prostate MR Images , 2016, IEEE Transactions on Medical Imaging.

[10]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[11]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

[12]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[13]  Alireza Behrad,et al.  Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network , 2012, Biomed. Signal Process. Control..

[14]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[15]  Reza Azmi,et al.  An Improved Retinal Vessel Segmentation Method Based on High Level Features for Pathological Images , 2014, Journal of Medical Systems.

[16]  Inas A. Yassine,et al.  Convolutional neural networks for deep feature learning in retinal vessel segmentation , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[17]  Marc'Aurelio Ranzato,et al.  Large Scale Distributed Deep Networks , 2012, NIPS.

[18]  Marcin Ciecholewski,et al.  Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods , 2017, Symmetry.

[19]  Hamid Abrishami Moghaddam,et al.  A novel method for retinal vessel tracking using particle filters , 2013, Comput. Biol. Medicine.

[20]  Max Mignotte,et al.  Superpixel and multi‐atlas based fusion entropic model for the segmentation of X‐ray images , 2018, Medical Image Anal..

[21]  Zhi-Hua Zhou,et al.  Dropout Rademacher complexity of deep neural networks , 2014, Science China Information Sciences.

[22]  Dar-Ren Chen,et al.  Watershed segmentation for breast tumor in 2-D sonography. , 2004, Ultrasound in medicine & biology.

[23]  Patrick Gallinari,et al.  SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent , 2009, J. Mach. Learn. Res..

[24]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[25]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[26]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[27]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.