A novel unsupervised segmentation method for overlapping cervical cell images

2017 ◽  
Author(s):  
Lili Zhao ◽  
Jianping Yin ◽  
Yongkai Ye ◽  
Kuan Li ◽  
Minghui Qiu
Keyword(s):  
Segmentation Method ◽  
Unsupervised Segmentation ◽  
Cervical Cell

scholarly journals Unsupervised segmentation and classification of cervical cell images

2012 ◽  
Vol 45 (12) ◽  
pp. 4151-4168 ◽  
Author(s):  
Aslı Gençtav ◽  
Selim Aksoy ◽  
Sevgen Önder
Keyword(s):  
Unsupervised Segmentation ◽  
Cervical Cell

Unsupervised segmentation method for cuboidal cell nuclei in histological prostate images based on minimum cross entropy

2013 ◽  
Vol 40 (18) ◽  
pp. 7331-7340 ◽  
Author(s):  
Domingos Lucas Latorre de Oliveira ◽  
Marcelo Zanchetta do Nascimento ◽  
Leandro Alves Neves ◽  
Moacir Fernandes de Godoy ◽  
Pedro Francisco Ferraz de Arruda ◽  
...  
Keyword(s):  
Cross Entropy ◽  
Segmentation Method ◽  
Cell Nuclei ◽  
Unsupervised Segmentation ◽  
Cuboidal Cell ◽  
Minimum Cross Entropy

scholarly journals SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 383 ◽  
Author(s):  
Talha Ilyas ◽  
Abbas Khan ◽  
Muhammad Umraiz ◽  
Hyongsuk Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Enhancement ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Segmentation Method ◽  
Unsupervised Segmentation ◽  
Segmentation Algorithms ◽  
Salient Features ◽  
The Given

Supervised semantic segmentation algorithms have been a hot area of exploration recently, but now the attention is being drawn towards completely unsupervised semantic segmentation. In an unsupervised framework, neither the targets nor the ground truth labels are provided to the network. That being said, the network is unaware about any class instance or object present in the given data sample. So, we propose a convolutional neural network (CNN) based architecture for unsupervised segmentation. We used the squeeze and excitation network, due to its peculiar ability to capture the features’ interdependencies, which increases the network’s sensitivity to more salient features. We iteratively enable our CNN architecture to learn the target generated by a graph-based segmentation method, while simultaneously preventing our network from falling into the pit of over-segmentation. Along with this CNN architecture, image enhancement and refinement techniques are exploited to improve the segmentation results. Our proposed algorithm produces improved segmented regions that meet the human level segmentation results. In addition, we evaluate our approach using different metrics to show the quantitative outperformance.

scholarly journals Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic Review

2020 ◽  
Author(s):  
Ramya Balakrishnan ◽  
Maria Valdes Hernandez ◽  
Andrew Farrall
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
White Matter ◽  
Clinical Research ◽  
White Matter Hyperintensities ◽  
Segmentation Method ◽  
Unsupervised Segmentation ◽  
Vascular Origin ◽  
Automatic Methods ◽  
Fully Automatic

Background: White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological conditions, through to healthy ageing. Fully automatic WMH quantification methods are promising, but still, traditional semi-automatic methods seem to be preferred in clinical research. We systematically reviewed the literature for fully automatic methods developed in the last five years, to assess what are considered state-of-the-art techniques, as well as trends in the analysis of WMH of presumed vascular origin. Method: We registered the systematic review protocol with the International Prospective Register of Systematic Reviews (PROSPERO), registration number - CRD42019132200. We conducted the search for fully automatic methods developed from 2015 to July 2020 on Medline, Science direct, IEE Explore, and Web of Science. We assessed risk of bias and applicability of the studies using QUADAS 2. Results: The search yielded 2327 papers after removing 104 duplicates. After screening titles, abstracts and full text, 37 were selected for detailed analysis. Of these, 16 proposed a supervised segmentation method, 10 proposed an unsupervised segmentation method, and 11 proposed a deep learning segmentation method. Average DSC values ranged from 0.538 to 0.91, being the highest value obtained from an unsupervised segmentation method. Only four studies validated their method in longitudinal samples, and eight performed an additional validation using clinical parameters. Only 8/37 studies made available their method in public repositories. Conclusions: We found no evidence that favours deep learning methods over the more established k-NN, linear regression and unsupervised methods in this task. Data and code availability, bias in study design and ground truth generation influence the wider validation and applicability of these methods in clinical research.

scholarly journals CONTOUR EXTRACTION OF PLANAR ELEMENTS OF BUILDING FACADES FROM POINT CLOUDS USING GLOBAL GRAPH-BASED CLUSTERING

2019 ◽  
Vol IV-2/W7 ◽  
pp. 211-219 ◽  
Author(s):  
Y. Xu ◽  
U. Stilla
Keyword(s):  
Graphical Model ◽  
Point Clouds ◽  
Contour Extraction ◽  
Segmentation Method ◽  
Unsupervised Segmentation ◽  
Line Segments ◽  
Segmentation Methods ◽  
Building Facades ◽  
Urban Scene ◽  
Building Elements

<p><strong>Abstract.</strong> In this work, we present a surface-based method to extract the contours of planar building elements in the urban scene. A bottom-up segmentation method that utilizes global graph-based optimization and supervoxel structure is developed, enabling an automatic and unsupervised segmentation of point clouds. Then, a planarity-based extraction is conducted to segments, and only the planar segments, as well as their neighborhoods, are selected as candidates for the plane fitting. The points of the plane can be identified by the parametric model given by the planarity calculation. Afterward, the boundary points of the extracted plane are extracted by the alpha-shape. Optionally, line segments can be fitted and optimized by the energy minimization with the local graphical model. The experimental results using different datasets reveal that our proposed segmentation methods can be effective and comparable with other method, and the contours of planar building elements can be well extracted from the complex urban scene.</p>

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