Automatic counting of trypanosomatid amastigotes in infected human cells

Relli, Cleber de Souza; Facon, Jacques; Britto, Alceu de Souza

doi:10.1016/j.compbiomed.2017.08.010

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…However, in accordance with the state of the art, the importance of using computational techniques in similar applications can be emphasized, such as a work related to the semi-automatic counting of amastigote nests by mathematical morphology; although in other studies the authors do not perform nest detection, they show a precision of 0.91 and 0.96, an accuracy of 0.83 and 0.86, as well as accuracy rates of 0.85 ± 0.10, recall rates of 0.86 ± 0.11%, and error rates of 0.16 ± 0.08 [33]. A similar work has also been presented about amastigote counting in Chagas-infected cells using unsupervised automatic classification techniques and morphological granulometric processing, obtaining in its test models error rates of 0.10 and 0.26, precision of 0.76 and 0.85, recall 0.61 and 0.78, as well as F-measures of 0.66 and 0.75, respectively [34]. Therefore, we consider that our results are objectively comparable to those already reported, also considering that automatic nest detection is being carried out.…”

Section: Resultsmentioning

confidence: 60%

Deep Learning–Based Segmentation of Trypanosoma cruzi Nests in Histopathological Images

Hevia-Montiel,

Haro,

Guillermo-Cordero

et al. 2023

Electronics

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The use of artificial intelligence has shown good performance in the medical imaging area, in particular the deep learning methods based on convolutional neural networks for classification, detection, and/or segmentation tasks. The task addressed in this research work is the segmentation of amastigote nests from histological microphotographs in the study of Trypanosoma cruzi infection (Chagas disease) implementing a U-Net convolutional network architecture. For the nests’ segmentation, a U-Net architecture was trained on histological images of an acute-stage murine experimental model performing a 5-fold cross-validation, while the final tests were carried out with data unseen by the U-Net from three image groups of different experimental models. During the training stage, the obtained results showed an average accuracy of 98.19 ± 0.01, while in the case of the final tests, an average accuracy of 99.9 ± 0.1 was obtained for the control group, as well as 98.8 ± 0.9 and 99.1 ± 0.8 for two infected groups; in all cases, high sensitivity and specificity were observed in the results. We can conclude that the use of a U-Net architecture proves to be a relevant tool in supporting the diagnosis and analysis of histological images for the study of Chagas disease.

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Section: Resultsmentioning

confidence: 60%

Deep Learning–Based Segmentation of Trypanosoma cruzi Nests in Histopathological Images

Hevia-Montiel,

Haro,

Guillermo-Cordero

et al. 2023

Electronics

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“…Cell counting methods for non-red blood cells are also available. In [40], a self-counting mechanism is proposed for flagellate trypanosomes infecting human cells: Firstly, morphological pre-treatment removes complex image backgrounds; secondly, unsupervised classification is used to segment collections; thirdly, threshold processing is used to preserve infected cells; and finally, cells are processed by morphological treatment and filtered by average. In [41], a robust segmentation method is proposed for counting milk somatic cells on a microscope slide image.…”

Section: Related Workmentioning

confidence: 99%

Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets

et al. 2019

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The isolation and observation of cardiomyocytes serve as the fundamental approach to cardiovascular research. The state-of-the-practice for the isolation and observation relies on manual operation of the entire culture process. Such a manual approach not only incurs high human errors, but also takes a long period of time. This paper proposes a new computer-aided paradigm to automatically, accurately, and efficiently perform the clustering and counting of cardiomyocytes, one of the key procedures for evaluating the success rate of cardiomyocytes isolation and the quality of culture medium. The key challenge of the proposed method lies in the unique, rod-like shape of cardiomyocytes, which has been hardly addressed in literature. Our proposed method employs a novel algorithm inspired by hesitant fuzzy sets and integrates an efficient implementation into the whole process of analyzing cardiomyocytes. The system, along with the data extracted from adult rats’ cardiomyocytes, has been experimentally evaluated with Matlab, showing promising results. The false accept rate (FAR) and the false reject rate (FRR) are as low as 1.46% and 1.97%, respectively. The accuracy rate is up to 98.7%—20% higher than the manual approach—and the processing time is reduced from tens of seconds to 3–5 s—an order of magnitude performance improvement.

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“…Although the motion is an intrinsic and indispensable biological aspect of T. cruzi, this characteristic still needs to be better explored and investigated using computational approaches to aid the study and medical diagnosis of Chagas disease. Among the approaches used to detect these parasites [18][19][20][21][22][23][24][25][26][27][28][29][30][31], few studies consider motion [18,19,25,26,29,31]. The dynamic context involved in the optical microscopy of blood samples infected with T. cruzi parasites is one of the challenges encountered by these approaches, which can be sensitive to different stimuli [29].…”

Section: Introductionmentioning

confidence: 99%

Trajectory-driven computational analysis for element characterization in Trypanosoma cruzi video microscopy

Martins,

Ferreira,

Carneiro

et al. 2024

PLoS ONE

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Optical microscopy videos enable experts to analyze the motion of several biological elements. Particularly in blood samples infected with Trypanosoma cruzi (T. cruzi), microscopy videos reveal a dynamic scenario where the parasites’ motions are conspicuous. While parasites have self-motion, cells are inert and may assume some displacement under dynamic events, such as fluids and microscope focus adjustments. This paper analyzes the trajectory of T. cruzi and blood cells to discriminate between these elements by identifying the following motion patterns: collateral, fluctuating, and pan–tilt–zoom (PTZ). We consider two approaches: i) classification experiments for discrimination between parasites and cells; and ii) clustering experiments to identify the cell motion. We propose the trajectory step dispersion (TSD) descriptor based on standard deviation to characterize these elements, outperforming state-of-the-art descriptors. Our results confirm motion is valuable in discriminating T. cruzi of the cells. Since the parasites perform the collateral motion, their trajectory steps tend to randomness. The cells may assume fluctuating motion following a homogeneous and directional path or PTZ motion with trajectory steps in a restricted area. Thus, our findings may contribute to developing new computational tools focused on trajectory analysis, which can advance the study and medical diagnosis of Chagas disease.

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Automatic counting of trypanosomatid amastigotes in infected human cells

Cited by 5 publications

References 11 publications

Deep Learning–Based Segmentation of Trypanosoma cruzi Nests in Histopathological Images

Deep Learning–Based Segmentation of Trypanosoma cruzi Nests in Histopathological Images

Toward Automatic Cardiomyocyte Clustering and Counting through Hesitant Fuzzy Sets

Trajectory-driven computational analysis for element characterization in Trypanosoma cruzi video microscopy

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