Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation

Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may cause blindness if the patient's condition is severe. Clinical results show that SWS can be divided into two types based on the characteristics of scleral blood vessels. Therefore, how to accurately segment scleral blood vessels has become a significant problem in computer-aided diagnosis. In this research, we propose to continuously upsample the bottom layer's feature maps to preserve image details, and design a novel Claw UNet based on UNet for scleral blood vessel segmentation. Specifically, the residual structure is used to increase the number of network layers in the feature extraction stage to learn deeper features. In the decoding stage, by fusing the features of the encoding, upsampling, and decoding parts, Claw UNet can achieve effective segmentation in the fine-grained regions of scleral blood vessels. To effectively extract small blood vessels, we use the attention mechanism to calculate the attention coefficient of each position in images. Claw UNet outperforms other UNet-based networks on scleral blood vessel image dataset.

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

[2]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Ashirbani Saha,et al.  Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing , 2018, Medical physics.

[5]  Zhiyi Luo,et al.  An Improved Breast Cancer Nuclei Segmentation Method Based on UNet++ , 2020, ICCAI.

[6]  Pedro Furtado,et al.  Segmentation of Eye Fundus Images by density clustering in diabetic retinopathy , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[7]  Yaxing Wang,et al.  S-UNet: A Bridge-Style U-Net Framework With a Saliency Mechanism for Retinal Vessel Segmentation , 2019, IEEE Access.

[8]  P. Gu,et al.  Early Trabeculotomy Ab Externo in Treatment of Sturge-Weber Syndrome. , 2017, American journal of ophthalmology.

[9]  Wenyi Guo,et al.  Episcleral hemangioma distribution patterns could be an indicator of trabeculotomy prognosis in young SWS patients. , 2020, Acta ophthalmologica.

[10]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[11]  Alessandro Lambiase,et al.  Ocular manifestations of Sturge–Weber syndrome: pathogenesis, diagnosis, and management , 2016, Clinical ophthalmology.

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

[13]  A. Plateroti,et al.  Sturge-Weber Syndrome Associated with Monolateral Ocular Melanocytosis, Iris Mammillations, and Diffuse Choroidal Haemangioma , 2017, Case Reports in Ophthalmology.

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

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

[16]  Pheng-Ann Heng,et al.  Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation , 2019, Front. Genet..

[17]  Zhiming Luo,et al.  Weighted Res-UNet for High-Quality Retina Vessel Segmentation , 2018, 2018 9th International Conference on Information Technology in Medicine and Education (ITME).

[18]  Yuesheng Zhu,et al.  Arnet: Attention-Based Refinement Network for Few-Shot Semantic Segmentation , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).