利用免疫组化、多光子显微镜和机器学习量化大鼠膀胱平滑肌的区域组织结构

Alireza Asadbeygi, Yasutaka Tobe, Naoki Yoshimura, Sean D. Stocker, Simon Watkins, Paul Watton, Anne M. Robertson
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引用次数: 0

摘要

膀胱中的平滑肌束(SMB)是膀胱有效排尿的收缩元件。与骨骼肌不同的是,这些肌束并非高度排列整齐,而是在整个膀胱壁上均匀分布。在这项研究中,首次对整个膀胱的 SMB 的区域定向进行了量化,而无需进行光学清除或冷冻切片。免疫组化染色被用来观察多光子显微镜(MPM)图像中膀胱平滑肌束(SMB)的平滑肌细胞肌动蛋白。利用一系列滤波器生成了每个像素的特征向量,包括高斯模糊、高斯梯度幅度、高斯拉普拉斯、Hessianeigenvalues、结构张量特征值、Gabor 和 Sobel 梯度。随后对随机森林分类器进行了训练,以自动分割 MPM 图像中的 SMB。最后,使用 CT-FIRE 软件包量化了每个膀胱区域中 SMB 的方向。这些信息对于包含收缩元件的膀胱生物力学模型至关重要。
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Quantifying Smooth Muscles Regional Organization in the Rat Bladder Using Immunohistochemistry, Multiphoton Microscopy and Machine Learning
The smooth muscle bundles (SMBs) in the bladder act as contractile elements which enable the bladder to void effectively. In contrast to skeletal muscles, these bundles are not highly aligned, rather they are oriented more heterogeneously throughout the bladder wall. In this work, for the first time, this regional orientation of the SMBs is quantified across the whole bladder, without the need for optical clearing or cryosectioning. Immunohistochemistry staining was utilized to visualize smooth muscle cell actin in multiphoton microscopy (MPM) images of bladder smooth muscle bundles (SMBs). Feature vectors for each pixel were generated using a range of filters, including Gaussian blur, Gaussian gradient magnitude, Laplacian of Gaussian, Hessian eigenvalues, structure tensor eigenvalues, Gabor, and Sobel gradients. A Random Forest classifier was subsequently trained to automate the segmentation of SMBs in the MPM images. Finally, the orientation of SMBs in each bladder region was quantified using the CT-FIRE package. This information is essential for biomechanical models of the bladder that include contractile elements.
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