Faster R-CNN based microscopic cell detection

Yang, Su; Fang, Bin; Tang, Wei; Wu, Xuegang; Qian, Jiang; Wu, Yang

doi:10.1109/spac.2017.8304302

Cited by 28 publications

(13 citation statements)

References 23 publications

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“…In addition, for their best model, they also use internal markers from watershed algorithm to separate the cells. In Yang et al [38], the authors detect and classify cells using Faster R-CNN [39]. They use microscopic images with size equal to 659 × 493 which is considered the ideal size as input for Faster R-CNN network.…”

Section: A Related Workmentioning

confidence: 99%

“…In addition, for their best model, they also use internal markers from watershed algorithm to separate the cells. In Yang et al [38] , the authors detect and classify cells using Faster R-CNN [39] . They use microscopic images with size equal to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$659\times 493$\end{document} which is considered the ideal size as input for Faster R-CNN network.…”

Section: Introductionmentioning

confidence: 99%

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Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears

Kassim

Palaniappan

Yang

et al. 2021

IEEE J. Biomed. Health Inform.

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Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster or superpixel segmentation, followed by a second refinement stage Faster R-CNN for detecting small cell objects within the connected component clusters. RBCNet uses cell clustering instead of region proposals, which is robust to cell fragmentation, is highly scalable for detecting small objects or fine scale morphological structures in very large images, can be trained using non-overlapping tiles, and during inference is adaptive to the scale of cell-clusters with a low memory footprint. We tested our method on an archived collection of human malaria smears with nearly 200,000 labeled cells across 965 images from 193 patients, acquired in Bangladesh, with each patient contributing five images. Cell detection accuracy using RBCNet was higher than 97%. The novel dual cascade RBCNet architecture provides more accurate cell detections because the foreground cell-cluster masks from U-Net adaptively guide the Manuscript

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“…In addition, for their best model, they also use internal markers from watershed algorithm to separate the cells. In Yang et al [38], the authors detect and classify cells using Faster R-CNN [39]. They use microscopic images with size equal to 659 × 493 which is considered the ideal size as input for Faster R-CNN network.…”

Section: A Related Workmentioning

confidence: 99%

“…In addition, for their best model, they also use internal markers from watershed algorithm to separate the cells. In Yang et al [38] , the authors detect and classify cells using Faster R-CNN [39] . They use microscopic images with size equal to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$659\times 493$\end{document} which is considered the ideal size as input for Faster R-CNN network.…”

Section: Introductionmentioning

confidence: 99%

Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears

Kassim

Palaniappan

Yang

et al. 2021

IEEE J. Biomed. Health Inform.

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“…The Faster R-CNN network also has two output layers. The first one is the Softmax classifier layer and the other is the regression layer that gives the accuracy of the detected area [14].…”

Section: Faster R-cnnmentioning

confidence: 99%

Defect detection of seals in multilayer aseptic packages using deep learning

Adem¹,

Közkurt²

2019

Turk J Elec Eng & Comp Sci

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Sealing in aseptic packages, one of the healthiest and cheapest technologies to protect food from parasites in the liquid food industry, requires a detailed and careful control process. Since the controls are made manually and visually by expert machine operators, the human factor can lead to the failure to detect defects, resulting in high cost and food safety risks. Therefore, this study aims to perform a leak test in aseptic package seals by a system that makes decisions using independent deep learning methods. The proposed Faster R-CNN and the Updated Faster R-CNN deep learning models were subjected to training and testing with a total of 400 images taken from a real production environment, resulting in a correct classification rate of 99.25%. As a result, it can be said that the study is the second study that performs a computer-aided quality control process with promising results, having distinctive features such as being the first study that conducts analysis using the deep learning method.

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“…A diferencia de las estrategias desarrolladas para la detección de objetos en vehículos autónomos donde se analizan imágenes en entornos naturales, en los sistemas CAD es necesario considerar las características de diferentes tipos de imágenes, adquiridas en condiciones más controladas y con equipos especializados como por ejemplo: resonancia magnética, tomografía axial computarizada, ultrasonido y microscopía (patología), entre otros. Muchos de estos métodos se inspiran en estrategias de detección de objetos con ventana deslizante (Ȓíha et al, 2013;Tek, 2013;Jung et al, 2013;Dženan et al, 2014;He et al, 2018), otros en conjuntos de regiones candidatas (Cireşan et al, 2013;Ma et al, 2017), y recientemente, en estrategias de aprendizaje profundo (Jiamin et al, 2017;Akselrod-Ballin et al, 2016;Kisilev et al, 2016;Sa et al, 2017;Yang et al, 2017;Heo et al, 2017) Ríha et al (2013) adaptan VJ para detectar el corte transversal de la arteria carótida en imágenes de ultrasonido obtenidas en modo B simple y para la descripción de las regiones emplean las características de Haar y el coeficiente de Matthews. Además, durante el entrenamiento del detector en cascada se utiliza el algoritmo de AdaBoost con una estrategia evolutiva.…”

Section: Diagnóstico Clínico Asistido Por Computadorunclassified

Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

Chaves

Saikia

Fernández-Robles

et al. 2018

Rev. iberoam. autom. inform. ind.

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<p>Actualmente, muchas aplicaciones requieren localizar de forma precisa los objetos que aparecen en una imagen, para su posterior procesamiento. Este es el caso de la inspección visual en la industria, los sistemas de diagnóstico clínico asistido por computador, la detección de obstáculos en vehículos o en robots, entre otros. Sin embargo, diversos factores como la calidad de la imagen y la apariencia de los objetos a detectar, dificultan la localización automática. En este artículo realizamos una revisión sistemática de los principales métodos utilizados para localizar objetos, considerando desde los métodos basados en ventanas deslizantes, como el detector propuesto por Viola y Jones, hasta los métodos actuales que usan redes de aprendizaje profundo, tales como Faster-RCNNo Mask-RCNN. Para cada propuesta, describimos los detalles relevantes, considerando sus ventajas y desventajas, así como sus aplicaciones en diversas áreas. El artículo pretende proporcionar una revisión ordenada y condensada del estado del arte de estas técnicas, su utilidad y sus implementaciones a fin de facilitar su conocimiento y uso por cualquier investigador que requiera localizar objetos en imágenes digitales. Concluimos este trabajo resumiendo las ideas presentadas y discutiendo líneas de trabajo futuro.</p>

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Faster R-CNN based microscopic cell detection

Cited by 28 publications

References 23 publications

Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears

Clustering-Based Dual Deep Learning Architecture for Detecting Red Blood Cells in Malaria Diagnostic Smears

Defect detection of seals in multilayer aseptic packages using deep learning

Una Revisión Sistemática de Métodos para Localizar Automáticamente Objetos en Imágenes

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