VSSC Net: Vessel Specific Skip chain Convolutional Network for blood vessel segmentation

BACKGROUND AND OBJECTIVE Deep learning techniques are instrumental in developing network models that aid in the early diagnosis of life-threatening diseases. To screen and diagnose the retinal fundus and coronary blood vessel disorders, the most important step is the proper segmentation of the blood vessels. METHODS This paper aims to segment the blood vessels from both the coronary angiogram and the retinal fundus images using a single VSSC Net after performing the image-specific preprocessing. The VSSC Net uses two-vessel extraction layers with added supervision on top of the base VGG-16 network. The vessel extraction layers comprise of the vessel-specific convolutional blocks to localize the blood vessels, skip chain convolutional layers to enable rich feature propagation, and a unique feature map summation. Supervision is associated with the two-vessel extraction layers using separate loss/sigmoid function. Finally, the weighted fusion of the individual loss/sigmoid function produces the desired blood vessel probability map. It is then binary segmented and validated for performance. RESULTS The VSSC Net shows improved accuracy values on the standard retinal and coronary angiogram datasets respectively. The computational time required to segment the blood vessels is 0.2 seconds using GPU. Moreover, the vessel extraction layer uses a lesser parameter count of 0.4 million parameters to accurately segment the blood vessels. CONCLUSION The proposed VSSC Net that segments blood vessels from both the retinal fundus images and coronary angiogram can be used for the early diagnosis of vessel disorders. Moreover, it could aid the physician to analyze the blood vessel structure of images obtained from multiple imaging sources.

[1]  Mark Fisher,et al.  Retinal vessel segmentation using multi-scale textons derived from keypoints , 2015, Comput. Medical Imaging Graph..

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

[3]  Wang Yongtian,et al.  Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking , 2010, Biomedical engineering online.

[4]  András Hajdu,et al.  Segmentation of retinal vessels by means of directional response vector similarity and region growing , 2015, Comput. Biol. Medicine.

[5]  Hari Sundar,et al.  A robust and accurate approach to automatic blood vessel detection and segmentation from angiography x-ray images using multistage random forests , 2012, Medical Imaging.

[6]  C. Paterson,et al.  Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program. , 2009, Investigative ophthalmology & visual science.

[7]  Yi Wang,et al.  Retinal blood vessel segmentation using fully convolutional network with transfer learning , 2018, Comput. Medical Imaging Graph..

[8]  Erik J. Bekkers,et al.  Retinal vessel delineation using a brain-inspired wavelet transform and random forest , 2017, Pattern Recognit..

[9]  Song Guo,et al.  Deeply supervised neural network with short connections for retinal vessel segmentation , 2018, Int. J. Medical Informatics.

[10]  Yuan Zhang,et al.  Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function , 2018, Neurocomputing.

[11]  Zengchang Qin,et al.  Automated identification and grading of coronary artery stenoses with X-ray angiography , 2018, Comput. Methods Programs Biomed..

[12]  S Pearl Mary,et al.  Unified adaptive framework for contrast enhancement of blood vessels , 2020 .

[13]  Ahad Harati,et al.  Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation , 2017, Comput. Methods Programs Biomed..

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

[15]  Bunyarit Uyyanonvara,et al.  Blood vessel segmentation methodologies in retinal images - A survey , 2012, Comput. Methods Programs Biomed..

[16]  Zengchang Qin,et al.  Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging. , 2018, Computer methods and programs in biomedicine.

[17]  Hua Ma,et al.  Vessel layer separation in x-ray angiograms with fully convolutional network , 2018, Medical Imaging.

[18]  Mingchen Gao,et al.  Deep vessel tracking: A generalized probabilistic approach via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[19]  Pearl Mary Samuel,et al.  Multilevel and Multiscale Deep Neural Network for Retinal Blood Vessel Segmentation , 2019, Symmetry.

[20]  R. Klein,et al.  Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality. , 2001, Survey of ophthalmology.

[21]  Jianhuang Wu,et al.  A Novel Method of Vessel Segmentation for X-ray Coronary Angiography Images , 2012, 2012 Fourth International Conference on Computational and Information Sciences.

[22]  Shahab Aslani,et al.  A new supervised retinal vessel segmentation method based on robust hybrid features , 2016, Biomed. Signal Process. Control..

[23]  Mohammad H. Jafari,et al.  Segmentation of vessels in angiograms using convolutional neural networks , 2018, Biomed. Signal Process. Control..

[24]  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.

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

[26]  Xin Yang,et al.  A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation , 2019, IEEE Journal of Biomedical and Health Informatics.

[27]  Mohammad H. Jafari,et al.  Vessel extraction in X-ray angiograms using deep learning , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[28]  Lei Zhang,et al.  Multi-level deep supervised networks for retinal vessel segmentation , 2017, International Journal of Computer Assisted Radiology and Surgery.

[29]  Imdad Ali Ismaili,et al.  Supervised method for blood vessel segmentation from coronary angiogram images using 7-D feature vector , 2016 .

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

[31]  Tuan D. Pham,et al.  DUNet: A deformable network for retinal vessel segmentation , 2018, Knowl. Based Syst..

[32]  Fernando Cervantes-Sanchez,et al.  Automatic Segmentation of Coronary Arteries in X-ray Angiograms using Multiscale Analysis and Artificial Neural Networks , 2019, Applied Sciences.

[33]  M. S. Avila-Garcia,et al.  A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms , 2018, Journal of healthcare engineering.

[34]  Sang Jun Park,et al.  Scale-space approximated convolutional neural networks for retinal vessel segmentation , 2019, Comput. Methods Programs Biomed..

[35]  Carlos Fernandez-Lozano,et al.  Automatic multiscale vascular image segmentation algorithm for coronary angiography , 2018, Biomed. Signal Process. Control..

[36]  J. Alison Noble,et al.  Tramline and NP windows estimation for enhanced unsupervised retinal vessel segmentation , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[37]  Jiang Liu,et al.  Dense Dilated Network With Probability Regularized Walk for Vessel Detection , 2019, IEEE Transactions on Medical Imaging.

[38]  A. Osareh,et al.  Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering , 2014, Journal of medical signals and sensors.

[39]  Xiaoyi Jiang,et al.  A self-adaptive matched filter for retinal blood vessel detection , 2014, Machine Vision and Applications.

[40]  Rajeev Srivastava,et al.  Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter , 2016, Comput. Methods Programs Biomed..

[41]  Keshab K. Parhi,et al.  Iterative Vessel Segmentation of Fundus Images , 2015, IEEE Transactions on Biomedical Engineering.

[42]  Elli Angelopoulou,et al.  Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database , 2013, IET Image Process..

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

[44]  Giri Babu Kande,et al.  Unsupervised Fuzzy Based Vessel Segmentation In Pathological Digital Fundus Images , 2010, Journal of Medical Systems.

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

[46]  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.

[47]  Sang Jun Park,et al.  Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks , 2018, Journal of Digital Imaging.

[48]  Paul Y. S. Cheung,et al.  Vessel Extraction Under Non-Uniform Illumination: A Level Set Approach , 2008, IEEE Transactions on Biomedical Engineering.

[49]  Zhen Chen,et al.  Morphological Multiscale Enhancement, Fuzzy Filter and Watershed for Vascular Tree Extraction in Angiogram , 2011, Journal of Medical Systems.

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

[51]  S Nirmala Devi,et al.  Comparison of active contour models for image segmentation in X-ray coronary angiogram images , 2008, Journal of medical engineering & technology.

[52]  Qi Tian,et al.  CcNet: A cross-connected convolutional network for segmenting retinal vessels using multi-scale features , 2020, Neurocomputing.

[53]  David Menotti,et al.  A Semi-Automatic Method for Segmentation of the Coronary Artery Tree from Angiography , 2009, 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing.

[54]  Elena De Momi,et al.  Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics , 2018, Comput. Methods Programs Biomed..

[55]  J.J. Bellanger,et al.  A Level Set Method for Vessel Segmentation in Coronary Angiography , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[56]  Kotagiri Ramamohanarao,et al.  An effective retinal blood vessel segmentation method using multi-scale line detection , 2013, Pattern Recognit..

[57]  Bram van Ginneken,et al.  Comparative study of retinal vessel segmentation methods on a new publicly available database , 2004, SPIE Medical Imaging.

[58]  Yongtian Wang,et al.  Saliency driven vasculature segmentation with infinite perimeter active contour model , 2017, Neurocomputing.

[59]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[60]  Saeed Sadri,et al.  Segmentation of Coronary Vessels by Combining the Detection of Centerlines and Active Contour Model , 2011, 2011 7th Iranian Conference on Machine Vision and Image Processing.

[61]  Danni Ai,et al.  Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-Ray Angiograms , 2018, IEEE Access.

[62]  Danni Ai,et al.  Automatic Coronary Artery Segmentation in X-ray Angiograms by Multiple Convolutional Neural Networks , 2018, ICMIP.