Tracking Neutrophil Migration in Zebrafish Model Using Multi-Channel Feature Learning

Moghadam, Marzieh R.; Chen, Yi‐Ping Phoebe

doi:10.1109/jbhi.2020.3019271

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…Through advanced machine learning algorithms used in analyzing microscopic video, real-time segmentation of the zebrafish atria and ventricles as well as dynamic reconstruction of heart and blood flow can be performed by analyzing the microscopic video, thus enabling the analysis of cardiac function indicators like heart rate, end-diastolic volume, end-systolic volume, ejection fraction, and shortening fraction. ,, Yet, the reliance on these advanced algorithms necessitates a thorough evaluation of their accuracy and the potential for overfitting or misinterpretation of complex biological phenomena. The development of a CNN-based multichannel feature learning model for neutrophil tracking and the use of the random forest algorithm for clustering analysis of drug-induced changes in neuronal activity further illustrate the growing dependence on computational methods in biological research. − …”

Section: Ai-based Video Analysismentioning

confidence: 99%

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening

Wang,

Dong,

Qiao

et al. 2024

Environ. Sci. Technol.

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The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

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Section: Ai-based Video Analysismentioning

confidence: 99%

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening

Wang,

Dong,

Qiao

et al. 2024

Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The motivation behind the employment of multiple modalities lies in the fact that the multimodal framework may tackle heterogeneities (e.g. diverse image contrasts, inconsistent signal-to-noise ratios and mismatched regulations) associated with ASD classification by combining complementary information, and eventually this may demonstrate superior classification performance to a single model framework [15,16]. First, each MRI modality is processed through a standard pre-processing pipeline.…”

Section: Introductionmentioning

confidence: 99%