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 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 Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning

2020 ◽  
Vol 10 (2) ◽  
pp. 615 ◽  
Author(s):  
Tomas Iesmantas ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Metric Learning ◽  
Cell Nuclei ◽  
Similarity Coefficients ◽  
Clinical Practices ◽  
Nuclei Segmentation ◽  
Wide Range ◽  
Triplet Loss ◽  
Deep Metric Learning

(1) Background: The segmentation of cell nuclei is an essential task in a wide range of biomedical studies and clinical practices. The full automation of this process remains a challenge due to intra- and internuclear variations across a wide range of tissue morphologies, differences in staining protocols and imaging procedures. (2) Methods: A deep learning model with metric embeddings such as contrastive loss and triplet loss with semi-hard negative mining is proposed in order to accurately segment cell nuclei in a diverse set of microscopy images. The effectiveness of the proposed model was tested on a large-scale multi-tissue collection of microscopy image sets. (3) Results: The use of deep metric learning increased the overall segmentation prediction by 3.12% in the average value of Dice similarity coefficients as compared to no metric learning. In particular, the largest gain was observed for segmenting cell nuclei in H&E -stained images when deep learning network and triplet loss with semi-hard negative mining were considered for the task. (4) Conclusion: We conclude that deep metric learning gives an additional boost to the overall learning process and consequently improves the segmentation performance. Notably, the improvement ranges approximately between 0.13% and 22.31% for different types of images in the terms of Dice coefficients when compared to no metric deep learning.

scholarly journals Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach

2021 ◽  
Vol 40 (2) ◽  
pp. 163-173
Author(s):  
Yemao Hou ◽  
Mario Canul-Ku ◽  
Xindong Cui ◽  
Rogelio Hasimoto-Beltran ◽  
Min Zhu
Keyword(s):  
Deep Learning ◽  
Evolutionary Biology ◽  
Semantic Segmentation ◽  
Stratigraphic Correlation ◽  
Segmentation Method ◽  
Research Areas ◽  
Performance Metric ◽  
Segmentation Methods ◽  
Ct Data ◽  
Tomography Data

Abstract. Vertebrate microfossils have broad applications in evolutionary biology and stratigraphy research areas such as the evolution of hard tissues and stratigraphic correlation. Classification is one of the basic tasks of vertebrate microfossil studies. With the development of techniques for virtual paleontology, vertebrate microfossils can be classified efficiently based on 3D volumes. The semantic segmentation of different fossils and their classes from CT data is a crucial step in the reconstruction of their 3D volumes. Traditional segmentation methods adopt thresholding combined with manual labeling, which is a time-consuming process. Our study proposes a deep-learning-based (DL-based) semantic segmentation method for vertebrate microfossils from CT data. To assess the performance of the method, we conducted extensive experiments on nearly 500 fish microfossils. The results show that the intersection over union (IoU) performance metric arrived at least 94.39 %, meeting the semantic segmentation requirements of paleontologists. We expect that the DL-based method could also be applied to other fossils from CT data with good performance.

scholarly journals A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images

2013 ◽  
Vol 14 (1) ◽  
Author(s):  
Long Chen ◽  
Leanne Lai Hang Chan ◽  
Zhongying Zhao ◽  
Hong Yan
Keyword(s):  
Time Lapse ◽  
Segmentation Method ◽  
Cell Nuclei ◽  
C Elegans ◽  
Nuclei Segmentation

scholarly journals Learning Cell Nuclei Segmentation Using Labels Generated With Classical Image Analysis Methods

2021 ◽  
Author(s):  
Damian J. Matuszewski ◽  
Petter Ranefall
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Biomedical Applications ◽  
Classical Method ◽  
Cell Nuclei ◽  
Dice Coefficient ◽  
Analysis Pipeline ◽  
Nuclei Segmentation ◽  
Classical Image ◽  
Better Than

Creating manual annotations in a large number of images is a tedious bottleneck that limits deep learning use in many applications. Here, we present a study in which we used the output of a classical image analysis pipeline as labels when training a convolutional neural network (CNN). This may not only reduce the time experts spend annotating images but it may also lead to an improvement of results when compared to the output from the classical pipeline used in training. In our application, i.e., cell nuclei segmentation, we generated the annotations using CellProfiler (a tool for developing classical image analysis pipelines for biomedical applications) and trained on them a U-Net-based CNN model. The best model achieved a 0.96 dice-coefficient of the segmented Nuclei and a 0.84 object-wise Jaccard index which was better than the classical method used for generating the annotations by 0.02 and 0.34, respectively. Our experimental results show that in this application, not only such training is feasible but also that the deep learning segmentations are a clear improvement compared to the output from the classical pipeline used for generating the annotations.

scholarly journals An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hwejin Jung ◽  
Bilal Lodhi ◽  
Jaewoo Kang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Gaussian Mixture ◽  
Color Variation ◽  
Post Processing ◽  
Segmentation Method ◽  
Deep Convolutional Neural Networks ◽  
Nuclei Segmentation ◽  
Histopathology Images

Abstract Background Since nuclei segmentation in histopathology images can provide key information for identifying the presence or stage of a disease, the images need to be assessed carefully. However, color variation in histopathology images, and various structures of nuclei are two major obstacles in accurately segmenting and analyzing histopathology images. Several machine learning methods heavily rely on hand-crafted features which have limitations due to manual thresholding. Results To obtain robust results, deep learning based methods have been proposed. Deep convolutional neural networks (DCNN) used for automatically extracting features from raw image data have been proven to achieve great performance. Inspired by such achievements, we propose a nuclei segmentation method based on DCNNs. To normalize the color of histopathology images, we use a deep convolutional Gaussian mixture color normalization model which is able to cluster pixels while considering the structures of nuclei. To segment nuclei, we use Mask R-CNN which achieves state-of-the-art object segmentation performance in the field of computer vision. In addition, we perform multiple inference as a post-processing step to boost segmentation performance. We evaluate our segmentation method on two different datasets. The first dataset consists of histopathology images of various organ while the other consists histopathology images of the same organ. Performance of our segmentation method is measured in various experimental setups at the object-level and the pixel-level. In addition, we compare the performance of our method with that of existing state-of-the-art methods. The experimental results show that our nuclei segmentation method outperforms the existing methods. Conclusions We propose a nuclei segmentation method based on DCNNs for histopathology images. The proposed method which uses Mask R-CNN with color normalization and multiple inference post-processing provides robust nuclei segmentation results. Our method also can facilitate downstream nuclei morphological analyses as it provides high-quality features extracted from histopathology images.

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.

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