scholarly journals Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Khin Yadanar Win ◽  
Somsak Choomchuay ◽  
Kazuhiko Hamamoto ◽  
Manasanan Raveesunthornkiat
Keyword(s):  
Pleural Effusion ◽  
Comparative Study ◽  
Performance Metrics ◽  
Mean Shift ◽  
Automated Analysis ◽  
Cell Nuclei ◽  
Detection Rates ◽  
Nuclei Segmentation ◽  
Segmentation Methods ◽  
Aided Diagnosis

Automated cell nuclei segmentation is the most crucial step toward the implementation of a computer-aided diagnosis system for cancer cells. Studies on the automated analysis of cytology pleural effusion images are few because of the lack of reliable cell nuclei segmentation methods. Therefore, this paper presents a comparative study of twelve nuclei segmentation methods for cytology pleural effusion images. Each method involves three main steps: preprocessing, segmentation, and postprocessing. The preprocessing and segmentation stages help enhancing the image quality and extracting the nuclei regions from the rest of the image, respectively. The postprocessing stage helps in refining the segmented nuclei and removing false findings. The segmentation methods are quantitatively evaluated for 35 cytology images of pleural effusion by computing five performance metrics. The evaluation results show that the segmentation performances of the Otsu, k-means, mean shift, Chan–Vese, and graph cut methods are 94, 94, 95, 94, and 93%, respectively, with high abnormal nuclei detection rates. The average computational times per image are 1.08, 36.62, 50.18, 330, and 44.03 seconds, respectively. The findings of this study will be useful for current and potential future studies on cytology images of pleural effusion.

scholarly journals Efficient Stain-Aware Nuclei Segmentation Deep Learning Framework for Multi-Center Histopathological Images

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 954
Author(s):  
Loay Hassan ◽  
Mohamed Abdel-Nasser ◽  
Adel Saleh ◽  
Osama A. Omer ◽  
Domenec Puig
Keyword(s):  
Deep Learning ◽  
Choquet Integral ◽  
Adjacent Cell ◽  
Cell Nuclei ◽  
Nuclei Segmentation ◽  
Learning Framework ◽  
Segmentation Methods ◽  
Histopathological Images ◽  
The Individual ◽  
Whole Slide Images

Existing nuclei segmentation methods have obtained limited results with multi-center and multi-organ whole-slide images (WSIs) due to the use of different stains, scanners, overlapping, clumped nuclei, and the ambiguous boundary between adjacent cell nuclei. In an attempt to address these problems, we propose an efficient stain-aware nuclei segmentation method based on deep learning for multi-center WSIs. Unlike all related works that exploit a single-stain template from the dataset to normalize WSIs, we propose an efficient algorithm to select a set of stain templates based on stain clustering. Individual deep learning models are trained based on each stain template, and then, an aggregation function based on the Choquet integral is employed to combine the segmentation masks of the individual models. With a challenging multi-center multi-organ WSIs dataset, the experimental results demonstrate that the proposed method outperforms the state-of-art nuclei segmentation methods with aggregated Jaccard index (AJI) and F1-scores of 73.23% and 89.32%, respectively, while achieving a lower number of parameters.

scholarly journals Efficient Multi-Organ Multi-Center Cell Nuclei Segmentation Method Based on Deep Learnable Aggregation Network

2021 ◽  
Vol 38 (3) ◽  
pp. 653-661
Author(s):  
Loay Hassan ◽  
Adel Saleh ◽  
Mohamed Abdel-Nasser ◽  
Osama A. Omer ◽  
Domenec Puig
Keyword(s):  
Deep Learning ◽  
Color Variation ◽  
Segmentation Method ◽  
Cell Nuclei ◽  
Nuclei Segmentation ◽  
Segmentation Methods ◽  
Cancer Subtype ◽  
Center Cell ◽  
Cancer Grading ◽  
Segmentation Models

Automated cell nuclei delineation in whole-slide imaging (WSI) is a fundamental step for many tasks like cancer cell recognition, cancer grading, and cancer subtype classification. Although numerous computational methods have been proposed for segmenting nuclei in WSI images based on image processing and deep learning, existing approaches face major challenges such as color variation due to the use of different stains, the various structures of cell nuclei, and the overlapping and clumped cell nuclei. To circumvent these challenges in this article, we propose an efficient and accurate cell nuclei segmentation method based on deep learning, in which a set of accurate individual cell nuclei segmentation models are developed to predict rough segmentation masks, and then a learnable aggregation network (LANet) is used to predict the final nuclei masks. Besides, we develop cell nuclei segmentation software (with a graphical user interface—GUI) that includes the proposed method and other deep-learning-based cell nuclei segmentation methods. A challenging WSI dataset collected from different centers and organs is used to demonstrate the efficiency of our method. The experimental results reveal that our method obtains a competitive performance compared to the existing approaches in terms of the aggregated Jaccard index (AJI=89.25%) and F1-score (F1=73.02%). The developed nuclei segmentation software can be downloaded from https://github.com/loaysh2010/Cell-Nuclei-Segmentation-GUI-Application.

scholarly journals Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Khin Yadanar Win ◽  
Somsak Choomchuay ◽  
Kazuhiko Hamamoto ◽  
Manasanan Raveesunthornkiat ◽  
Likit Rangsirattanakul ◽  
...  
Keyword(s):  
Pleural Effusion ◽  
Cancer Cells ◽  
Texture Features ◽  
Computer Aided Diagnosis ◽  
Classification Model ◽  
Morphological Operations ◽  
Gray Level ◽  
Cell Nuclei ◽  
Computer Aided ◽  
Aided Diagnosis

Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Aided Diagnosis (CAD) system for the detection of cancer cells in cytological pleural effusion (CPE) images. Firstly, intensity adjustment and median filtering methods were applied to improve image quality. Cell nuclei were extracted through a hybrid segmentation method based on the fusion of Simple Linear Iterative Clustering (SLIC) superpixels and K-Means clustering. A series of morphological operations were utilized to correct segmented nuclei boundaries and eliminate any false findings. A combination of shape analysis and contour concavity analysis was carried out to detect and split any overlapped nuclei into individual ones. After the cell nuclei were accurately delineated, we extracted 14 morphometric features, 6 colorimetric features, and 181 texture features from each nucleus. The texture features were derived from a combination of color components based first order statistics, gray level cooccurrence matrix and gray level run-length matrix. A novel hybrid feature selection method based on simulated annealing combined with an artificial neural network (SA-ANN) was developed to select the most discriminant and biologically interpretable features. An ensemble classifier of bagged decision trees was utilized as the classification model for differentiating cells into either benign or malignant using the selected features. The experiment was carried out on 125 CPE images containing more than 10500 cells. The proposed method achieved sensitivity of 87.97%, specificity of 99.40%, accuracy of 98.70%, and F-score of 87.79%.

scholarly journals Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 15
Author(s):  
Subrata Bhattacharjee ◽  
Kobiljon Ikromjanov ◽  
Kouayep Sonia Carole ◽  
Nuwan Madusanka ◽  
Nam-Hoon Cho ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Cluster Analysis ◽  
Performance Metrics ◽  
Minimum Spanning Tree ◽  
Cell Nuclei ◽  
Model Quality ◽  
Statistical Parameters ◽  
Nuclei Segmentation ◽  
Grade Groups

Biomarker identification is very important to differentiate the grade groups in the histopathological sections of prostate cancer (PCa). Assessing the cluster of cell nuclei is essential for pathological investigation. In this study, we present a computer-based method for cluster analyses of cell nuclei and performed traditional (i.e., unsupervised method) and modern (i.e., supervised method) artificial intelligence (AI) techniques for distinguishing the grade groups of PCa. Two datasets on PCa were collected to carry out this research. Histopathology samples were obtained from whole slides stained with hematoxylin and eosin (H&E). In this research, state-of-the-art approaches were proposed for color normalization, cell nuclei segmentation, feature selection, and classification. A traditional minimum spanning tree (MST) algorithm was employed to identify the clusters and better capture the proliferation and community structure of cell nuclei. K-medoids clustering and stacked ensemble machine learning (ML) approaches were used to perform traditional and modern AI-based classification. The binary and multiclass classification was derived to compare the model quality and results between the grades of PCa. Furthermore, a comparative analysis was carried out between traditional and modern AI techniques using different performance metrics (i.e., statistical parameters). Cluster features of the cell nuclei can be useful information for cancer grading. However, further validation of cluster analysis is required to accomplish astounding classification results.

scholarly journals Recent computational methods for white blood cell nuclei segmentation: A comparative study

2019 ◽  
Vol 173 ◽  
pp. 1-14 ◽  
Author(s):  
Alan R. Andrade ◽  
Luis H.S. Vogado ◽  
Rodrigo de M.S. Veras ◽  
Romuere R.V. Silva ◽  
Flávio H.D. Araujo ◽  
...  
Keyword(s):  
Blood Cell ◽  
Comparative Study ◽  
Computational Methods ◽  
White Blood Cell ◽  
Cell Nuclei ◽  
Nuclei Segmentation

scholarly journals Comparative Study: Statistical Approach and Deep Learning Method for Automatic Segmentation Methods for Lung CT Image Segmentation

2020 ◽  
Vol 2 (4) ◽  
pp. 187-193
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Comparative Study ◽  
Performance Metrics ◽  
Automatic Segmentation ◽  
Ct Image ◽  
Learning Method ◽  
Segmentation Methods ◽  
Sensitivity Specificity ◽  
Lung Ct

Recently, deep learning technique is playing important starring role for image segmentation field in medical imaging of accurate tasks. In a critical component of diagnosis, deep learning is an organized network with homogeneous areas to provide accurate results. It is proved its superior quality with statistical model automatic segmentation methods in many critical condition environments. In this research article, we focus the improved accuracy and speed of the system process compared with conservative automatic segmentation methods. Also we compared performance metrics such as accuracy, sensitivity, specificity, precision, RMSE, Precision- Recall Curve with different algorithm in deep learning method. This comparative study covers the constructing an efficient and accurate model for Lung CT image segmentation.

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.

scholarly journals A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

2021 ◽  
Vol 21 (4) ◽  
pp. 1-22
Author(s):  
Safa Otoum ◽  
Burak Kantarci ◽  
Hussein Mouftah
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Comparative Study ◽  
Performance Metrics ◽  
Action Learning ◽  
Smart Devices ◽  
Critical Infrastructures ◽  
State Action ◽  
Detection Techniques ◽  
Depth Analysis

Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .

Sign in / Sign up

Export Citation Format

Share Document