Overlapping cell nuclei segmentation in microscopic images using deep belief networks

Duggal, Rahul; Gupta, Anubha; Gupta, Ritu; Wadhwa, Manya; Ahuja, Chirag

doi:10.1145/3009977.3010043

Cited by 57 publications

(23 citation statements)

References 8 publications

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“…To overcome the limitation of using a single dataset and to broaden the scope of our work, we extended our study to a second, independent and more recent dataset, C-NMC [30][31][32] . This dataset was used for the B-ALL normal versus malignant cell classification challenge at IEEE ISBI-2019 and consists of a large number of labeled images of normal and malignant cells.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…This dataset was used for the B-ALL normal versus malignant cell classification challenge at IEEE ISBI-2019 and consists of a large number of labeled images of normal and malignant cells. The cell images were extracted from blood smear microscopy images after normalizing the stain, as described in [30][31][32] . The total size of the training dataset is 10,661 images from 76 subjects.…”

Section: Dataset Descriptionmentioning

confidence: 99%

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Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Sahlol

Kollmannsberger

Ewees

2020

Sci Rep

185

101

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White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.

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Section: Dataset Descriptionmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Sahlol

Kollmannsberger

Ewees

2020

Sci Rep

185

101

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“…They trained the network using edge enhanced images, centered on cells detected during an initial detection stage, and combined them together with manually annotated cell boundaries. Duggal et al utilized a form of generative model known as a deep belief network for separating touching or overlapping white blood cell nuclei from leukemia in microscopy images. Recently, Haering et al presented a cycle‐consistent GAN (Cycle‐GAN) for segmenting epithelial cell tissue in drosophila embryos.…”

Section: Deep Learning For Image Cytometrymentioning

confidence: 99%

Deep Learning in Image Cytometry: A Review

et al. 2018

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Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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“…As described in the contest (https://competitions.codalab.org/competitions/ 20429#learn_the_details) [2,4], the initial dataset consists of 76 subjects, among which 47 subjects suffer ALL and the rest are healthy. Totally, there are 10,661 cells, in which 7,272 cells are classified as ALL and 3,389 cells are classified as HEM.…”

Section: Details Of Initial Datasetmentioning

confidence: 99%

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

Liu¹,

Long²

2019

Preprint

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Acute lymphoblastic leukemia (ALL) is a blood cancer which leads 111,000 depth globally in 2015. Recently, diagnosing ALL often involves the microscopic image analysis with the help of deep learning (DL) techniques. However, as most medical related problems, deficiency training samples and minor visual difference between ALL and normal cells make the image analysis task quite challenging. Herein, an augmented image enhanced bagging ensemble learning with elaborately designed training subsets were proposed to tackle above challenges. The weighted F1-scores of preliminary test set and final test are 0.84 and 0.88 respectively employing our ensemble model predictions and ranked within top 10% in ISBI-2019 Classification of Normal vs. Malignant White Blood Cancer Cells contest. Our results preliminarily show the efficacy and accuracy of employing DL based techniques in ALL cells image analysis.

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Overlapping cell nuclei segmentation in microscopic images using deep belief networks

Cited by 57 publications

References 8 publications

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

Deep Learning in Image Cytometry: A Review

Acute lymphoblastic leukemia cells image analysis with deep bagging ensemble learning

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