Micro-Vessel Image Segmentation Based on the AD-UNet Model

Retinal vessel segmentation plays a vital role in computer-aided diagnosis and treatment of retinal diseases. Considering the low contrast between retinal vessels and the background image, complex structural information as well as blurred boundaries between tissue and blood vessels, the retinal vessel image segmentation algorithm based on the improved U-Net network is proposed in the paper. The algorithm introduces an attention mechanism and densely connected network into the original U-Net network and realizes the automatic segmentation of retinal vessels. According to the test results of the algorithm on commonly-used datasets of the DRIVE and STARE fundus images, respectively, the accuracy is 0.9663 and 0.9684; the sensitivity is 0.8075 and 0.8437; the specificity is 0.9814 and 0.9762; the AUC values are 0.9846 and 0.9765; and the F-measures are 0.8203 and 0.8419, respectively. In the paper, the Attention-Dense-UNet (AD-UNet) algorithm is applied to segment human bulbar conjunctival micro-vessels. The experimental results show that the algorithm can achieve ideal segmentation results.

[1]  Wang Xiao-hong,et al.  Automatic Segmentation for Retinal Vessel Based on Multi-scale 2D Gabor Wavelet , 2015 .

[2]  Ning Tan,et al.  Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network , 2019, IEEE Access.

[3]  Tianfu Wang,et al.  A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images , 2016, IEEE Transactions on Medical Imaging.

[4]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[5]  Ana Maria Mendonça,et al.  Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction , 2006, IEEE Transactions on Medical Imaging.

[6]  Hong Zhang,et al.  A Fundus Retinal Vessels Segmentation Scheme Based on the Improved Deep Learning U-Net Model , 2019, IEEE Access.

[7]  Yongtian Wang,et al.  Retinal vascular segmentation using superpixel‐based line operator and its application to vascular topology estimation , 2018, Medical physics.

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

[9]  Sang Jun Park,et al.  Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks , 2017, ArXiv.

[10]  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).

[11]  Yuan Hong,et al.  Automatic segmentation of levator hiatus from ultrasound images using U-net with dense connections , 2019, Physics in medicine and biology.

[12]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[13]  Giri Babu Kande,et al.  Retinal vessel segmentation using histogram matching , 2008, APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems.

[14]  Vijayan K. Asari,et al.  Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation , 2018, ArXiv.

[15]  Krzysztof Krawiec,et al.  Segmenting Retinal Blood Vessels With Deep Neural Networks , 2016, IEEE Transactions on Medical Imaging.

[16]  Matthew B. Blaschko,et al.  A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images , 2017, IEEE Transactions on Biomedical Engineering.

[17]  Yang Du,et al.  Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism , 2019, IEEE Access.

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

[19]  José Manuel Bravo,et al.  A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features , 2011, IEEE Transactions on Medical Imaging.

[20]  Yi Yu,et al.  Dense U-net Based on Patch-Based Learning for Retinal Vessel Segmentation , 2019, Entropy.

[21]  Luc Van Gool,et al.  Deep Retinal Image Understanding , 2016, MICCAI.

[22]  Yanna Zhao,et al.  Drusen Segmentation From Retinal Images via Supervised Feature Learning , 2018, IEEE Access.

[23]  Manoranjan Paul,et al.  Contrast normalization steps for increased sensitivity of a retinal image segmentation method , 2017, Signal, Image and Video Processing.

[24]  Junbin Gao,et al.  Automatic retinal vessel extraction algorithm based on contrast-sensitive schemes , 2016, 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ).

[25]  M. Usman Akram,et al.  Blood Vessel Enhancement and Segmentation Using Wavelet Transform , 2009, 2009 International Conference on Digital Image Processing.

[26]  Américo Oliveira,et al.  Retinal vessel segmentation based on Fully Convolutional Neural Networks , 2018, Expert Syst. Appl..

[27]  Susanto Rahardja,et al.  A New Hybrid Algorithm for Retinal Vessels Segmentation on Fundus Images , 2019, IEEE Access.