CNL-UNet: A novel lightweight deep learning architecture for multimodal biomedical image segmentation with false output suppression

2021 ◽  
Vol 70 ◽  
pp. 102959
Author(s):  
Md. Badiuzzaman Shuvo ◽  
Rifat Ahommed ◽  
Sakib Reza ◽  
M.M.A. Hashem
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
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.

scholarly journals Biomedical Image Segmentation via Representative Annotation

2019 ◽  
Vol 33 ◽  
pp. 5901-5908 ◽  
Author(s):  
Hao Zheng ◽  
Lin Yang ◽  
Jianxu Chen ◽  
Jun Han ◽  
Yizhe Zhang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Active Learning ◽  
Iterative Process ◽  
Image Data ◽  
Manual Annotation ◽  
Convolutional Network ◽  
Biomedical Image ◽  
Image Patches ◽  
Representative Image

Deep learning has been applied successfully to many biomedical image segmentation tasks. However, due to the diversity and complexity of biomedical image data, manual annotation for training common deep learning models is very timeconsuming and labor-intensive, especially because normally only biomedical experts can annotate image data well. Human experts are often involved in a long and iterative process of annotation, as in active learning type annotation schemes. In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation. RA uses unsupervised networks for feature extraction and selects representative image patches for annotation in the latent space of learned feature descriptors, which implicitly characterizes the underlying data while minimizing redundancy. A fully convolutional network (FCN) is then trained using the annotated selected image patches for image segmentation. Our RA scheme offers three compelling advantages: (1) It leverages the ability of deep neural networks to learn better representations of image data; (2) it performs one-shot selection for manual annotation and frees annotators from the iterative process of common active learning based annotation schemes; (3) it can be deployed to 3D images with simple extensions. We evaluate our RA approach using three datasets (two 2D and one 3D) and show our framework yields competitive segmentation results comparing with state-of-the-art methods.

DDeep3M: Docker-powered deep learning for biomedical image segmentation

2020 ◽  
Vol 342 ◽  
pp. 108804
Author(s):  
Xinglong Wu ◽  
Shangbin Chen ◽  
Jin Huang ◽  
Anan Li ◽  
Rong Xiao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Biomedical Image

scholarly journals Deep learning approaches to biomedical image segmentation

2020 ◽  
Vol 18 ◽  
pp. 100297 ◽  
Author(s):  
Intisar Rizwan I Haque ◽  
Jeremiah Neubert
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Approaches ◽  
Biomedical Image

Biomedical Image Segmentation by Deep Learning Methods

2021 ◽  
pp. 131-154
Author(s):  
K. Anita Davamani ◽  
C.R. Rene Robin ◽  
S. Amudha ◽  
L. Jani Anbarasi
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Methods ◽  
Biomedical Image

scholarly journals nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

2020 ◽  
Author(s):  
Fabian Isensee ◽  
Paul F. Jaeger ◽  
Simon A. A. Kohl ◽  
Jens Petersen ◽  
Klaus H. Maier-Hein
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Biomedical Image

scholarly journals Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation

2020 ◽  
Vol 22 (1) ◽  
pp. 127-153
Author(s):  
John A. Onofrey ◽  
Lawrence H. Staib ◽  
Xiaojie Huang ◽  
Fan Zhang ◽  
Xenophon Papademetris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Deep Learning ◽  
Computational Efficiency ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Data Driven ◽  
High Dimensional ◽  
Biomedical Image ◽  
Selection Of

Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification.

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