Automatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images

Suzuki, Celso Tetsuo Nagase; Gomes, Jancarlo Ferreira; Falcão, Alexandre X.; Papa, João Paulo; Hoshino‐Shimizu, Sumie

doi:10.1109/tbme.2012.2187204

Cited by 77 publications

(58 citation statements)

References 23 publications

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“…Different software solutions have been proposed for the automated identification and quantification of parasites, such as soil-transmitted helminths, in digitized samples with various levels of reported sensitivity and specificity [31,32]. Deep learning-based solutions to this type of pattern recognition tasks represent the state of the art in machine learning, and have recently attained significant attention due to the high performance of such algorithms in various image classification tasks [33–35].…”

Section: Discussionmentioning

confidence: 99%

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

Holmström

Linder

Ngasala

et al. 2017

Global Health Action

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Background: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis. Objective: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images. Methods: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs. Results: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3–100%) in the test set (n = 217) of manually labeled helminth eggs. Conclusions: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images.

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Section: Discussionmentioning

confidence: 99%

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

Holmström

Linder

Ngasala

et al. 2017

Global Health Action

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“…More detailed descriptions about the supervised OPF algorithm and some of its recent applications can be found, for example, in [36]-classification of ultrasonic signals, [47]-land cover classification, [48,49]-electroencephalogram (EEG) and electrocardiogram (ECG) signal identification and recognition, [50]-characterization of graphite particles in metallographic images, [51,52]-learning-time constrained applications, [53]-segmentation and classification of human intestinal parasites, and in [54]-intrusion detection in computer networks.…”

Section: Machine Learningmentioning

confidence: 99%

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Silva

Marinho

Filho

et al. 2016

Metals

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Duplex stainless steels present excellent mechanical and corrosion resistance properties. However, when heat treated at temperatures above 600 • C, the undesirable tertiary sigma phase is formed. This phase presents high hardness, around 900 HV, and it is rich in chromium, the material toughness being compromised when the amount of this phase is not less than 4%. This work aimed to develop a solution for the detection of this phase in duplex stainless steels through the computational classification of induced magnetic field signals. The proposed solution is based on an Optimum Path Forest classifier, which was revealed to be more robust and effective than Bayes, Artificial Neural Network and Support Vector Machine based classifiers. The induced magnetic field was produced by the interaction between an applied external field and the microstructure. Samples of the 2205 duplex stainless steel were thermal aged in order to obtain different amounts of sigma phases (up to 18% in content). The obtained classification results were compared against the ones obtained by Charpy impact energy test, amount of sigma phase, and analysis of the fracture surface by scanning electron microscopy and X-ray diffraction. The proposed solution achieved a classification accuracy superior to 95% and was revealed to be robust to signal noise, being therefore a valid testing tool to be used in this domain.

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“…Figures 1 and 2 show examples of the 15 different classes of parasites. In order to reduce the quantity of impurities on the slides, in [10] we adopted the TF-Test parasitological technique, which is used in more than 45 public and private health laboratories in Brazil. In this work we used the TF-Test Modified [11], which can provide a considerable reduction in the number of impurities (see Figure 3b).…”

Section: Parasitological Techniquementioning

confidence: 99%

“…In [10], we proposed a first solution for automatic identification of the 15 most common species of protozoa and helminths in Brazil. Our system also depended on manual image acquisition and focus, and the image analysis technique was computationally expensive, hence unsuitable for laboratory routine.…”

Section: Introductionmentioning

confidence: 99%

Automated diagnosis of human intestinal parasites using optical microscopy images

Suzuki¹,

Gomes

Falcão

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes.

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Automatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images

Cited by 77 publications

References 23 publications

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium

Classification of Induced Magnetic Field Signals for the Microstructural Characterization of Sigma Phase in Duplex Stainless Steels

Automated diagnosis of human intestinal parasites using optical microscopy images

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