Overview of Current Biomedical Image Segmentation Methods

2016 ◽  
Author(s):  
Maedeh Sadat Fasihi ◽  
Wasfy B. Mikhael
Keyword(s):  
Image Segmentation ◽  
Biomedical Image ◽  
Segmentation Methods

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.

scholarly journals Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks

2019 ◽  
Vol 33 ◽  
pp. 8417-8424 ◽  
Author(s):  
Xiang He ◽  
Sibei Yang ◽  
Guanbin Li ◽  
Haofeng Li ◽  
Huiyou Chang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Contextual Information ◽  
Local Context ◽  
Deep Convolutional Neural Networks ◽  
Biomedical Image ◽  
Feature Representations ◽  
Segmentation Methods ◽  
Spatial Dependencies ◽  
Non Local

Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention. However, its vulnerability towards adversarial samples cannot be overlooked. This paper is the first one that discovers that all the CNN-based state-of-the-art biomedical image segmentation models are sensitive to adversarial perturbations. This limits the deployment of these methods in safety-critical biomedical fields. In this paper, we discover that global spatial dependencies and global contextual information in a biomedical image can be exploited to defend against adversarial attacks. To this end, non-local context encoder (NLCE) is proposed to model short- and long-range spatial dependencies and encode global contexts for strengthening feature activations by channel-wise attention. The NLCE modules enhance the robustness and accuracy of the non-local context encoding network (NLCEN), which learns robust enhanced pyramid feature representations with NLCE modules, and then integrates the information across different levels. Experiments on both lung and skin lesion segmentation datasets have demonstrated that NLCEN outperforms any other state-of-the-art biomedical image segmentation methods against adversarial attacks. In addition, NLCE modules can be applied to improve the robustness of other CNN-based biomedical image segmentation methods.

scholarly journals A General System for Automatic Biomedical Image Segmentation Using Intensity Neighborhoods

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Cheng Chen ◽  
John A. Ozolek ◽  
Wei Wang ◽  
Gustavo K. Rohde
Keyword(s):  
Image Segmentation ◽  
Learning Strategy ◽  
General System ◽  
Training Data ◽  
Tissue Segmentation ◽  
Biomedical Image ◽  
Nuclei Segmentation ◽  
Segmentation Methods ◽  
Microscopy Images ◽  
Better Than

Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.

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

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

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
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