Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension

Kurnia, Kevin Adi; Sampurna, Bonifasius Putera; Audira, Gilbert; Juniardi, Stevhen; Vasquez, Ross D.; Roldan, Marri Jmelou M.; Tsao, Che-Chia; Hsiao, Chung-Der

doi:10.3390/ijms23116009

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…However, the Watershed method showed overcounting in most cases. Previously, Watershed has been proposed as a common method used for separation, especially in cell-related studies [36,52]. However, in this study, the birds were normally not circular-/oval-shaped; thus, this might have compromised the object separation of the Watershed method.…”

Section: Discussionmentioning

confidence: 88%

Performance Comparison of Five Methods Available in ImageJ for Bird Counting and Detection from Video Datasets

Kurnia,

Saputra,

Luong

et al. 2024

Inventions

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Bird monitoring is an important approach to studying the diversity and abundance of birds, especially during migration, as it can provide core data for bird conservation purposes. The previous methods for bird number estimation are largely based on manual counting, which suffers from low throughput and a high error rate. In this study, we aimed to provide an alternative bird-counting method from video datasets by using five available ImageJ methods: Particle Analyzer, Find Maxima, Watershed segmentation, TrackMate, and trainable WEKA segmentation. The numbers of birds and their XY coordinates were extracted from videos to conduct a side-by-side comparison with the manual counting results, and the three important criteria of the sensitivity, precision, and F1 score were calculated for the performance evaluation. From the tests, which we conducted for four different cases with different bird numbers or flying patterns, TrackMate had the best overall performance for counting birds and pinpointing their locations, followed by Particle Analyzer, Find Maxima, WEKA, and lastly, Watershed, which showed low precision in most of the cases. In summary, five ImageJ-based counting methods were compared in this study, and we validated that TrackMate obtains the best performance for bird counting and detection.

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Section: Discussionmentioning

confidence: 88%

Performance Comparison of Five Methods Available in ImageJ for Bird Counting and Detection from Video Datasets

Kurnia,

Saputra,

Luong

et al. 2024

Inventions

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“…To test the recording setup and analyzing workflow, we firstly performed the locomotion analysis on the wild-type T. thermophila CU428 strain without any special treatment. All videos were captured by a high-resolution CCD mounted onto an upright microscope with a 4× objective lens [ 41 ]. Since a good video recording process is crucial to ensure optimal tracking performance, we found that videos with a resolution of 3840 × 2160 pixels and a frame rate of 30 frames per second could provide an optimal recording and achieve high contrast suitable for TRex tracking and analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The video was recorded for 10 s at 30 frames per second (fps) and saved in the .mp4 format. The recording setup was established based on a previous publication [ 41 ].…”

Section: Methodsmentioning

confidence: 99%

Establishing a High-Throughput Locomotion Tracking Method for Multiple Biological Assessments in Tetrahymena

Suryanto

Vasquez

Roldan

et al. 2022

Cells

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Protozoa are eukaryotic, unicellular microorganisms that have an important ecological role, are easy to handle, and grow rapidly, which makes them suitable for ecotoxicity assessment. Previous methods for locomotion tracking in protozoa are largely based on software with the drawback of high cost and/or low operation throughput. This study aimed to develop an automated pipeline to measure the locomotion activity of the ciliated protozoan Tetrahymena thermophila using a machine learning-based software, TRex, to conduct tracking. Behavioral endpoints, including the total distance, velocity, burst movement, angular velocity, meandering, and rotation movement, were derived from the coordinates of individual cells. To validate the utility, we measured the locomotor activity in either the knockout mutant of the dynein subunit DYH7 or under starvation. Significant reduction of locomotion and alteration of behavior was detected in either the dynein mutant or in the starvation condition. We also analyzed how Tetrahymena locomotion was affected by the exposure to copper sulfate and showed that our method indeed can be used to conduct a toxicity assessment in a high-throughput manner. Finally, we performed a principal component analysis and hierarchy clustering to demonstrate that our analysis could potentially differentiate altered behaviors affected by different factors. Taken together, this study offers a robust methodology for Tetrahymena locomotion tracking in a high-throughput manner for the first time.

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“…In this study, we utilized StarDist for Wolffia globosa frond counting. The idea of using StarDist came from our previous study to count Tetrahymena cells with a circular shape [ 35 ]. The result of Wolffia detection using StarDist showed promising results, as presented in Figure 2 , with several misdetections due to light reflections on some parts of the microplate and reflection of Wolffia .…”

Section: Discussionmentioning

confidence: 99%

“…There have been studies that used artificial intelligence/machine learning methods to overcome contaminated water through machine learning applications [ 33 ] and quantify cell growth for toxicity studies in other species/cell lines [ 34 , 35 ]. The use of a machine learning-based method might be a solution to the limitation of ImageJ limitation as free software.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automatic Duckweed Counting Using StarDist and Its Application on Measuring Growth Inhibition Potential of Rare Earth Elements as Contaminants of Emerging Concerns

Kurnia,

Lin,

Farhan

et al. 2023

Toxics

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In recent years, there have been efforts to utilize surface water as a power source, material, and food. However, these efforts are impeded due to the vast amounts of contaminants and emerging contaminants introduced by anthropogenic activities. Herbicides such as Glyphosate and Glufosinate are commonly known to contaminate surface water through agricultural industries. In contrast, some emerging contaminants, such as rare earth elements, have started to enter the surface water from the production and waste of electronic products. Duckweeds are angiosperms from the Lemnaceae family and have been used for toxicity tests in aquatic environments, mainly those from the genus Lemna, and have been approved by OECD. In this study, we used duckweed from the genus Wolffia, which is smaller and considered a good indicator of metal pollutants in the aquatic environment. The growth rate of duckweed is the most common endpoint in observing pollutant toxicity. In order to observe and mark the fronds automatically, we used StarDist, a machine learning-based tool. StarDist is available as a plugin in ImageJ, simplifying and assisting the counting process. Python also helps arrange, manage, and calculate the inhibition percentage after duckweeds are exposed to contaminants. The toxicity test results showed Dysprosium to be the most toxic, with an IC50 value of 14.6 ppm, and Samarium as the least toxic, with an IC50 value of 279.4 ppm. In summary, we can provide a workflow for automatic frond counting using StarDist integrated with ImageJ and Python to simplify the detection, counting, data management, and calculation process.

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Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension

Cited by 4 publications

References 22 publications

Performance Comparison of Five Methods Available in ImageJ for Bird Counting and Detection from Video Datasets

Performance Comparison of Five Methods Available in ImageJ for Bird Counting and Detection from Video Datasets

Establishing a High-Throughput Locomotion Tracking Method for Multiple Biological Assessments in Tetrahymena

Deep Learning-Based Automatic Duckweed Counting Using StarDist and Its Application on Measuring Growth Inhibition Potential of Rare Earth Elements as Contaminants of Emerging Concerns

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