Mr. Jiaxiang Ren Profile

Jiaxiang Ren

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Proceedings Article | 2 April 2024 Poster + Paper

Weakly supervised detection of cell activation

Zhilin Zou, Kicheon Park, Jiaxiang Ren, Wensheng Cheng, Sophia Liu, Yingtian Pan, Congwu Du, Haibin Ling

Proceedings Volume 12930, 129301J (2024) https://doi.org/10.1117/12.3005371

KEYWORDS: Education and training, Feature extraction, Denoising, Neurons, Optical imaging, Calcium, In vivo imaging, Visualization, Neuroimaging, Interference (communication)

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Traditional stimulation-evoked cortical activity analysis mainly relies on the manual detection of the bright spots in a specific size regarded as active cells. However, there is limited research on the automatic detection of the cortical active cell in optical imaging of in vivo experiments which is much noisy. To address the laborious and hard annotation work, we propose a novel weakly supervised approach for active cell detection along the temporal frames. Compared to prevalent detection methods on common datasets, we formulate cell activation detection as a classification problem. We combine the techniques of clustering and deep neural network with little user indication of the Maximum Intensity Projection (MIP) of the time-lapse optical image sequence to realize the unsupervised classification model. The proposed approach achieves comparable performance on our optical image sequence with instant activation changing at each frame, which marks the cells using the fluorescent indicators. Although much noise is introduced during in vivo imaging, our algorithm is designed to accurately and effectively generate statistics on cell activation without requiring any prior training data preparation. This feature makes it particularly valuable for analyzing cell responses to psychopharmacological stimulation in subsequent analyses.

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