FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

2021 ◽  
Author(s):  
Bekhzod Olimov ◽  
Karshiev Sanjar ◽  
Sadia Din ◽  
Awaise Ahmad ◽  
Anand Paul ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Biomedical Image ◽  
Model Based

Target attack on biomedical image segmentation model based on multi-scale gradients

2021 ◽  
Vol 554 ◽  
pp. 33-46
Author(s):  
Mingwen Shao ◽  
Gaozhi Zhang ◽  
Wangmeng Zuo ◽  
Deyu Meng
Keyword(s):  
Image Segmentation ◽  
Biomedical Image ◽  
Multi Scale ◽  
Model Based

SEAUNet:Domain Adaptation for Biomedical Image Segmentation

2020 ◽  
Author(s):  
Hang Chen ◽  
Leiting Chen ◽  
Yuchu Chen ◽  
Minghao Fan ◽  
Chuan Zhou
Keyword(s):  
Image Segmentation ◽  
Biomedical Image

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

Biomedical Image Segmentation: A Survey

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Yahya Alzahrani ◽  
Boubakeur Boufama
Keyword(s):  
Image Segmentation ◽  
Biomedical Image

scholarly journals Capsules for biomedical image segmentation

2021 ◽  
Vol 68 ◽  
pp. 101889
Author(s):  
Rodney LaLonde ◽  
Ziyue Xu ◽  
Ismail Irmakci ◽  
Sanjay Jain ◽  
Ulas Bagci
Keyword(s):  
Image Segmentation ◽  
Biomedical Image

Visual Analytics for model-based medical image segmentation: Opportunities and challenges

2013 ◽  
Vol 40 (12) ◽  
pp. 4934-4943 ◽  
Author(s):  
Tatiana von Landesberger ◽  
Sebastian Bremm ◽  
Matthias Kirschner ◽  
Stefan Wesarg ◽  
Arjan Kuijper
Keyword(s):  
Image Segmentation ◽  
Visual Analytics ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Model Based

Biomedical Image Segmentation Using Spatial Kernel Fuzzy C-Means Based Level Set Formulation

2012 ◽  
Vol 2 (2) ◽  
pp. 200-205
Author(s):  
Raghotham Reddy Ganta ◽  
Syed Zaheeruddin ◽  
Narsimha Baddiri ◽  
R. Rameshwar Rao
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Biomedical Image ◽  
Level Set Formulation
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