Collateral motion saliency-based model for Trypanosoma cruzi detection in dye-free blood microscopy

Martins, Geovani L.; Ferreira, Daniel Silva; Ramalho, Geraldo Luis Bezerra

doi:10.1016/j.compbiomed.2021.104220

Cited by 2 publications

(11 citation statements)

References 22 publications

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“…We define this k value based on the video frame rate of 30 fps. Martins et al [ 29 ] introduced sequences of 25 frames to detect T. cruzi motion. Arias-del-Angel et al [ 13 ] used a 0-2s interval to define parasite motility patterns.…”

Section: Resultsmentioning

confidence: 99%

“…Here, we utilized the dataset introduced by Martins et al [ 29 ], which comprises optical microscopy videos of blood samples and trajectories of T. cruzi trypomastigotes. Videos are up to 10 seconds long, with a frame rate of approximately 30 frames per second (fps) and a resolution of 640 x 480 pixels.…”

Section: Methodsmentioning

confidence: 99%

“…The training and testing videos have distinct parasite trajectories. We performed the same procedure as Martins et al [ 29 ] to generate the trajectories of the cells. We randomly selected three cells for each parasite in the video as our sample.…”

Section: Methodsmentioning

confidence: 99%

“…We propose the trajectory step dispersion (TSD) descriptor to estimate the step length dispersion of trajectory segments. Since T. cruzi ’s motion is conspicuous in the scene [ 29 ] and this microorganism is the only one with self-motion, we consider that parasites and cells can be distinguished by analyzing the variance between different segments of their trajectories at constant time intervals. The TSD can be computed as follows for a trajectory segment consisting of k points: where the step length is l i = p i +1 − p i ∀ i ∈ [1, k − 1], and the mean is .…”

Section: Methodsmentioning

confidence: 99%

“…While parasites are mobile elements by nature, cells are inert and start to move under the influence of dynamic events, as observed in optical microscopy videos. We hypothesize that the dynamic context emphasized by Martins et al [ 29 ] generates distinct trajectory patterns between the biological elements and may be explored to characterize T. cruzi and blood cells by computational approaches. While collateral motion-stimulated blood cells exist, our study is restricted to cells that do not interact with parasites.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Martins,

Ferreira,

Carneiro

et al. 2024

PLoS ONE

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Trypanosoma cruzi Detection using LSTM Convolutional Autoencoder

Martins,

Ferreira,

Carneiro

et al. 2023

Anais Do XXIII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2023)

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The presence of Trypanosoma cruzi (T. cruzi) parasites in blood samples is proof of the medical diagnosis of Chagas disease. Since the motion of these microorganisms is conspicuous in optical microscopy videos, we propose a spatio-temporal autoencoder for anomaly detection caused by parasite motility. This approach includes a spatial feature extractor and a temporal sequencer ConvLSTM for learning the temporal evolution of the spatial features. We trained the autoencoder with no parasites videos to learn the normal pattern and measured the regularity score in test videos with parasites. Our results showed that an LSTM-based autoencoder may identify T. cruzi anomalous motion, being a promising method for detecting parasites in microscopy videos.

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Collateral motion saliency-based model for Trypanosoma cruzi detection in dye-free blood microscopy

Cited by 2 publications

References 22 publications

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

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

Trypanosoma cruzi Detection using LSTM Convolutional Autoencoder

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