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20 March 2014 3D reconstruction of digitized histological sections for vasculature quantification in the mouse hind limb
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In contrast to imaging modalities such as magnetic resonance imaging and micro computed tomography, digital histology reveals multiple stained tissue features at high resolution (0.25μm/pixel). However, the two-dimensional (2D) nature of histology challenges three-dimensional (3D) quantification and visualization of the different tissue components, cellular structures, and subcellular elements. This limitation is particularly relevant to the vasculature, which has a complex and variable structure within tissues. The objective of this study was to perform a fully automated 3D reconstruction of histology tissue in the mouse hind limb preserving the accurate systemic orientation of the tissues, stained with hematoxylin and immunostained for smooth muscle α actin. We performed a 3D reconstruction using pairwise rigid registrations of 5μm thick, paraffin-embedded serial sections, digitized at 0.25μm/pixel. Each registration was performed using the iterative closest points algorithm on blood vessel landmarks. Landmarks were vessel centroids, determined according to a signed distance map of each pixel to a decision boundary in hue-saturation-value color space; this decision boundary was determined based on manual annotation of a separate training set. Cell nuclei were then automatically extracted and corresponded to refine the vessel landmark registration. Homologous nucleus landmark pairs appearing on not more than two adjacent slides were chosen to avoid registrations which force curved or non-sectionorthogonal structures to be straight and section-orthogonal. The median accumulated target registration errors ± interquartile ranges for the vessel landmark registration, and the nucleus landmark refinement were 43.4±42.8μm and 2.9±1.7μm, respectively (p<0.0001). Fully automatic and accurate 3D rigid reconstruction of mouse hind limb histology imaging is feasible based on extracted vasculature and nuclei.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yiwen Xu, J. Geoffrey Pickering, Zengxuan Nong, Eli Gibson, and Aaron D. Ward "3D reconstruction of digitized histological sections for vasculature quantification in the mouse hind limb", Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410G (20 March 2014);

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