Unspecified Yimeng Dou Profile

Yimeng Dou

Grad Student at Univ of Wisconsin School of Medicine and Public Health

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Proceedings Article | 1 April 2024 Poster + Paper

Identifying trends in carotid artery health using strain indices

Maxwell Kiernan, Yimeng Dou, Yurim Lee, Carol Mitchell, Erica Fletcher, Mark Kliewer, Matthew Lee, Tomy Varghese

Proceedings Volume 12932, 129321A (2024) https://doi.org/10.1117/12.3006537

KEYWORDS: Arteries, Ultrasonography, Atherosclerosis, Image segmentation, Elastography, Visualization

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Non-invasive ultrasound imaging has been used to assess changes in the arterial wall such as changes in stiffness and the presence of plaque and its progression. Typically, stiffer arteries are thought to contribute to the progression of atherosclerosis. Therefore, it would be beneficial to find tools that assess and monitor changes in arterial stiffness to assist in the identification of cardiovascular diseases (CVD) and to help optimize the treatment of CVD risk factors for these individuals. The objective of this research is to use non-invasive ultrasound-based Lagrangian carotid strain imaging to assess arterial stiffness in determining CVD risk. Utilizing radiofrequency data from the right and left common carotid arteries (CCAs), we calculated the accumulated axial, lateral, and shear strain indices and the peak-to-peak mean strain values for segmented CCA walls. Data were collected from participants with carotid plaque and age-matched controls without plaque. Radiofrequency data from two cardiac cycles were used to calculate all strain measures. The Mann- Whitney U-Test was used to compare strain values between participants with plaque and participants without plaque. All mean strain values, except for the LCCA axial strain, were noted to be lower in the participants with plaque compared to the controls without plaque, although these findings were not statistically significantly different. . These findings suggest that participants with plaque may have stiffer arteries due to the lower strain values. This study needs to be performed in a larger cohort to see if these differences are statistically significantly different.

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Co-localization of fluorescent signals using deep learning with Manders overlapping coefficient

Yimeng Dou, Yi-Hua Tsai, Chih-Chieh Liu, Brad Hobson, Pamela Lein

Proceedings Volume 11596, 115963C (2021) https://doi.org/10.1117/12.2580650

KEYWORDS: Signal detection, Luminescence, Somatosensory cortex, Sensors, Photomicroscopy, Neurons, Fluorescence spectroscopy, Convolutional neural networks, Amygdala

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Object-based co-localization of uorescent signals allows the assessment of interactions between two (or more) biological entities using spatial information. It relies on object identification with high accuracy to separate uorescent signals from the background. Object detectors using convolutional neural networks (CNN) with annotated training samples could facilitate the process by detecting and counting fluorescent-labeled cells from uorescence photomicrographs. However, datasets containing segmented annotations of colocalized cells are generally not available, and creating a new dataset with delineated masks is label-intensive. Also, the colocalization coefficient is often not used as a component during training with the CNN model. Yet, it may aid with localizing and detecting objects during training and testing. In this work, we propose to address these issues by using a quantification coefficient for co-localization called Manders overlapping coefficient (MOC)¹ as a single-layer branch in a CNN. Fully convolutional one-state (FCOS)² with a Resnet101 backbone served as the network to evaluate the effectiveness of the novel branch to assist with bounding box prediction. Training data were sourced from lab curated uorescence images of neurons from the rat hippocampus, piriform cortex, somatosensory cortex, and amygdala. Results suggest that using modified FCOS with MOC outperformed the original FCOS model for accuracy in detecting uorescence signals by 1.1% in mean average precision (mAP). The model could be downloaded from https://github.com/Alphafrey946/Colocalization-MOC.

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