scholarly journals Microscopy cell nuclei segmentation with enhanced U-Net

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Feixiao Long
Keyword(s):  
Data Science ◽  
State Of The Art ◽  
Cell Nuclei ◽  
Nuclei Segmentation ◽  
Microscopy Image ◽  
Segmentation Algorithms ◽  
Bright Field Microscopy ◽  
The Cost ◽  
Advanced Computing ◽  
Microscopy Image Analysis

Abstract Background Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. Results An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch is proposed to potentially work with low-resources computing. Through strictly controlled experiments, the average IOU and precision of U-Net+ predictions are confirmed to outperform other prevalent competing methods with 1.0% to 3.0% gain on the first stage test set of 2018 Kaggle Data Science Bowl cell nuclei segmentation contest with shorter inference time. Conclusions Our results preliminarily demonstrate the potential of proposed U-Net+ in correctly spotting microscopy cell nuclei with resources-constraint computing.

scholarly journals AUTOMATIC OBJECT DETECTION AND SEGMENTATION OF THE HISTOCYTOLOGY IMAGES USING RESHAPABLE AGENTS

2013 ◽  
Vol 32 (2) ◽  
pp. 89 ◽  
Author(s):  
Mehdi Alilou ◽  
Vassili Kovalev
Keyword(s):  
Object Detection ◽  
Cost Function ◽  
State Of The Art ◽  
Automatic Detection ◽  
Target Cells ◽  
Segmentation Method ◽  
Microscopy Image ◽  
Image Dataset ◽  
The Cost ◽  
Prior Shape

The aim of this study is to suggest a method for automatic detection and segmentation of the target objects in the microscopic histology/cytology images. The detection is carried out by rectangular shapes then segmentation process starts utilizing flexible agents which are able to move and change their shapes according to a cost function. The agents are rectangular at the beginning then they gradually fit to the corresponding objects using a stochastic reshaping algorithm. The iterative reshaping process is controlled by a cost function and it is resulted in a finer segmentation of the target objects. The cost functional of the proposed method comprised of three terms including the prior shape, regional texture and gradient information. The experiments were carried out using a publicly available microscopy image dataset which contains 510 manually-labeled target cells. The segmentation performance of the proposed method is compared with another state of the art segmentation method. The results demonstrate satisfactory detection and segmentation performance of the proposed method.

scholarly journals Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl

2019 ◽  
Vol 16 (12) ◽  
pp. 1247-1253 ◽  
Author(s):  
Juan C. Caicedo ◽  
Allen Goodman ◽  
Kyle W. Karhohs ◽  
Beth A. Cimini ◽  
Jeanelle Ackerman ◽  
...  
Keyword(s):  
Biomedical Applications ◽  
Data Science ◽  
Human Interaction ◽  
Imaging Data ◽  
Cell Nuclei ◽  
Experimental Conditions ◽  
Microscopy Image ◽  
Bioimage Analysis ◽  
Microscopy Images ◽  
Nucleus Segmentation

Abstract Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.

scholarly journals An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario

2020 ◽  
Vol 10 (22) ◽  
pp. 7982
Author(s):  
Lorenzo Putzu ◽  
Giorgio Fumera
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Real World ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Training Data ◽  
Cell Nuclei ◽  
Nuclei Segmentation ◽  
Segmentation Methods

Cell nuclei segmentation is a challenging task, especially in real applications, when the target images significantly differ between them. This task is also challenging for methods based on convolutional neural networks (CNNs), which have recently boosted the performance of cell nuclei segmentation systems. However, when training data are scarce or not representative of deployment scenarios, they may suffer from overfitting to a different extent, and may hardly generalise to images that differ from the ones used for training. In this work, we focus on real-world, challenging application scenarios when no annotated images from a given dataset are available, or when few images (even unlabelled) of the same domain are available to perform domain adaptation. To simulate this scenario, we performed extensive cross-dataset experiments on several CNN-based state-of-the-art cell nuclei segmentation methods. Our results show that some of the existing CNN-based approaches are capable of generalising to target images which resemble the ones used for training. In contrast, their effectiveness considerably degrades when target and source significantly differ in colours and scale.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals Costs and Benefits of Autonomous Shipping—A Literature Review

2021 ◽  
Vol 11 (10) ◽  
pp. 4553
Author(s):  
Ewelina Ziajka-Poznańska ◽  
Jakub Montewka
Keyword(s):  
Literature Review ◽  
Business Models ◽  
Financial Models ◽  
State Of The Art ◽  
Costs And Benefits ◽  
Capital Costs ◽  
Research Activities ◽  
Benefit Estimation ◽  
Autonomous Ship ◽  
The Cost

The development of autonomous ship technology is currently in focus worldwide and the literature on this topic is growing. However, an in-depth cost and benefit estimation of such endeavours is in its infancy. With this systematic literature review, we present the state-of-the-art system regarding costs and benefits of the operation of prospective autonomous merchant ships with an objective for identifying contemporary research activities concerning an estimation of operating, voyage, and capital costs in prospective, autonomous shipping and vessel platooning. Additionally, the paper outlines research gaps and the need for more detailed business models for operating autonomous ships. Results reveal that valid financial models of autonomous shipping are lacking and there is significant uncertainty affecting the cost estimates, rendering only a reliable evaluation of specific case studies. The findings of this paper may be found relevant not only by academia, but also organisations considering to undertake a challenge of implementing Maritime Autonomous Surface Ships in their operations.

scholarly journals A review on the properties, reinforcing effects, and commercialization of nanomaterials for cement-based materials

2020 ◽  
Vol 9 (1) ◽  
pp. 303-322 ◽  
Author(s):  
Zhifang Zhao ◽  
Tianqi Qi ◽  
Wei Zhou ◽  
David Hui ◽  
Cong Xiao ◽  
...  
Keyword(s):  
Cement Hydration ◽  
State Of The Art ◽  
Research Progress ◽  
Market Survey ◽  
Future Perspectives ◽  
Nano Silica ◽  
Physical Processes ◽  
Existing Problems ◽  
Cement Based Materials ◽  
The Cost

AbstractThe behavior of cement-based materials is manipulated by chemical and physical processes at the nanolevel. Therefore, the application of nanomaterials in civil engineering to develop nano-modified cement-based materials is a promising research. In recent decades, a large number of researchers have tried to improve the properties of cement-based materials by employing various nanomaterials and to characterize the mechanism of nano-strengthening. In this study, the state of the art progress of nano-modified cement-based materials is systematically reviewed and summarized. First, this study reviews the basic properties and dispersion methods of nanomaterials commonly used in cement-based materials, including carbon nanotubes, carbon nanofibers, graphene, graphene oxide, nano-silica, nano-calcium carbonate, nano-calcium silicate hydrate, etc. Then the research progress on nano-engineered cementitious composites is reviewed from the view of accelerating cement hydration, reinforcing mechanical properties, and improving durability. In addition, the market and applications of nanomaterials for cement-based materials are briefly discussed, and the cost is creatively summarized through market survey. Finally, this study also summarizes the existing problems in current research and provides future perspectives accordingly.

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