English

Resource Constrained U-Net for Extraction of Retinal Vascular Trees

Image and Video Processing 2024-07-09 v1 Computer Vision and Pattern Recognition Machine Learning
Authors: Georgiy Kiselev
View on arXiv ↗ PDF ↗

Abstract

This paper demonstrates the efficacy of a modified U-Net structure for the extraction of vascular tree masks for human fundus photographs. On limited compute resources and training data, the proposed model only slightly underperforms when compared to state of the art methods.

Keywords

U-Net retinal vascular trees fundus photographs resource constrained

Cite

@article{arxiv.2407.04940,
  title  = {Resource Constrained U-Net for Extraction of Retinal Vascular Trees},
  author = {Georgiy Kiselev},
  journal= {arXiv preprint arXiv:2407.04940},
  year   = {2024}
}

Comments

8 pages

Related papers

View all related →
Image and Video Processing · Electrical Eng. & Systems
Parametric Scaling of Preprocessing assisted U-net Architecture for Improvised Retinal Vessel Segmentation
Kundan Kumar, Sumanshu Agarwal
2022-03-21
Image and Video Processing · Electrical Eng. & Systems
What Do We Really Need? Degenerating U-Net on Retinal Vessel Segmentation
Weilin Fu, Katharina Breininger, Zhaoya Pan, Andreas Maier
2019-11-11
Image and Video Processing · Electrical Eng. & Systems
Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation
Enes Sadi Uysal, M. Şafak Bilici, B. Selin Zaza, M. Yiğit Özgenç +1
2021-06-01
Computer Vision and Pattern Recognition · Computer Science
DUNet: A deformable network for retinal vessel segmentation
Qiangguo Jin, Zhaopeng Meng, Tuan D. Pham, Qi Chen +2
2019-05-21
Image and Video Processing · Electrical Eng. & Systems
Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network
Siyi Chen, Amir H. Kashani, Ji Yi
2024-02-16
Computer Vision and Pattern Recognition · Computer Science
Recurrent U-Net for Resource-Constrained Segmentation
Wei Wang, Kaicheng Yu, Joachim Hugonot, Pascal Fua +1
2019-06-13
Image and Video Processing · Electrical Eng. & Systems
Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models
Sojung Go, Younghoon Ji, Sang Jun Park, Soochahn Lee
2023-05-12
Image and Video Processing · Electrical Eng. & Systems
Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm
Jiahong Wei, Zhun Fan
2021-06-14
Image and Video Processing · Electrical Eng. & Systems
Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization
Jeremiah Fadugba, Patrick Köhler, Lisa Koch, Petru Manescu +1
2024-06-24
Image and Video Processing · Electrical Eng. & Systems
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
Adrian Galdran, André Anjos, José Dolz, Hadi Chakor +2
2020-09-07
Image and Video Processing · Electrical Eng. & Systems
The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation
Björn Browatzki, Jörn-Philipp Lies, Christian Wallraven
2020-11-26
Image and Video Processing · Electrical Eng. & Systems
Pyramid U-Net for Retinal Vessel Segmentation
Jiawei Zhang, Yanchun Zhang, Xiaowei Xu
2021-04-07
Image and Video Processing · Electrical Eng. & Systems
U-Net and its variants for medical image segmentation: theory and applications
Nahian Siddique, Paheding Sidike, Colin Elkin, Vijay Devabhaktuni
2021-06-10
Computer Vision and Pattern Recognition · Computer Science
Road Extraction by Deep Residual U-Net
Zhengxin Zhang, Qingjie Liu, Yunhong Wang
2018-05-23
Image and Video Processing · Electrical Eng. & Systems
Universal Vessel Segmentation for Multi-Modality Retinal Images
Bo Wen, Anna Heinke, Akshay Agnihotri, Dirk-Uwe Bartsch +3
2026-01-01
Computer Vision and Pattern Recognition · Computer Science
Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup
Artem Sevastopolsky, Stepan Drapak, Konstantin Kiselev, Blake M. Snyder +2
2018-11-22
Image and Video Processing · Electrical Eng. & Systems
Retinal Vessel Segmentation based on Fully Convolutional Networks
Zhengyuan Liu
2019-11-25
Image and Video Processing · Electrical Eng. & Systems
EViT-Unet: U-Net Like Efficient Vision Transformer for Medical Image Segmentation on Mobile and Edge Devices
Xin Li, Wenhui Zhu, Xuanzhao Dong, Oana M. Dumitrascu +1
2024-10-22
Computer Vision and Pattern Recognition · Computer Science
M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments
Tim Laibacher, Tillman Weyde, Sepehr Jalali
2019-04-24
Image and Video Processing · Electrical Eng. & Systems
Modeling and Enhancing Low-quality Retinal Fundus Images
Ziyi Shen, Huazhu Fu, Jianbing Shen, Ling Shao
2020-12-10
Image and Video Processing · Electrical Eng. & Systems
Contextual Information Enhanced Convolutional Neural Networks for Retinal Vessel Segmentation in Color Fundus Images
Muyi Sun, Guanhong Zhang
2021-03-26
Quantitative Methods · Quantitative Biology
Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features
Nanyan Zhu, Chen Liu, Zakary S. Singer, Tal Danino +2
2020-01-17
Image and Video Processing · Electrical Eng. & Systems
Implicit U-Net for volumetric medical image segmentation
Sergio Naval Marimont, Giacomo Tarroni
2022-07-01
Computer Vision and Pattern Recognition · Computer Science
MaskAttn-UNet: A Mask Attention-Driven Framework for Universal Low-Resolution Image Segmentation
Anzhe Cheng, Chenzhong Yin, Yu Chang, Heng Ping +3
2025-05-09
Image and Video Processing · Electrical Eng. & Systems
A deep learning based tool for automatic brain extraction from functional magnetic resonance images in rodents
Sidney Pontes-Filho, Annelene Gulden Dahl, Stefano Nichele, Gustavo Borges Moreno e Mello
2019-12-09