Baig, M. M. J. | Search bioRxiv

bioRxiv2026

An Integrated Multiphoton Imaging Workflow for Quantitative Analysis of Aortic Tissue Microstructure

Quantitative, reproducible characterization of aortic microstructure is essential for advancing vascular biomechanics and mechanobiology. To address this need, we present a comprehensive image-analysis workflow that extracts quantitative descriptors of tissue microstructure from multiphoton microscopy stacks of the murine thoracic aorta. Channel-specific signals are acquired for fibrillar collagen (second harmonic generation), elastin (two-photon autofluorescence), and cell nuclei (two-photon excited fluorescence). Following reorientation into the XZ plane, individual elastic lamellae are traced to quantify lamellar thickness and interlamellar spacing using circle-based geometry (Taubin fitting). After correction for vessel wall curvature via a cylindrical transformation, segmented nuclei are assigned to medial or adventitial compartments based on visual estimates of adventitial volume fraction, and nuclear morphology is characterized via ellipsoidal fitting in terms of nuclear aspect ratio and major-axis orientation. Collagen organization is resolved in XY sections by extracting fiber centerlines to quantify straightness and amplitude; traces from serial sections are then combined to reconstruct the three-dimensional collagen network and estimate porosity and linear fiber density, while fiber orientation distributions are derived from principal component analysis-based angles and fit using a von Mises mixture model. Finally, collagen and elastin volume fractions are computed via a two-stage fixed-threshold approach calibrated on a balanced training subset. Overall, this modular and robust workflow provides an integrated framework for studying aortic wall remodeling across physiological and pathological processes. Non-Technical SummaryAs the main blood vessel in our body, the aorta needs to be both strong and flexible. This balance comes from three main parts: elastic layers that allow the aorta to stretch, strong fibers that prevent tearing, and cells that sense and respond to changes in blood pressure and other signals. When any of these components are altered, the aorta may stiffen or weaken, which can interfere with normal blood flow. In this study, we developed a clear and consistent way to measure the structure of the aortic wall using microscope images. The approach examines how thick the elastic layers are and how far apart they lie, the size and orientation of cell centers, and how straight or wavy structural fibers appear. It also estimates how much of each component is present in the aortic wall. Because the same steps are applied each time, results can be fairly compared across different conditions. Overall, this tool transforms detailed images into simple measurements, helping scientists understand how the aorta changes in health and disease.

bioengineering↗

bioRxiv2025

User-Assisted Approach for Accurate Nonrigid Registration of Images and Traces

Fully automated registration algorithms are prone to getting trapped in solutions corresponding to local minima of their objective functions, leading to errors that are easy to detect but challenging to correct. Traditional solutions often involve iterative parameter tuning, data preprocessing and preregistering, and multiple algorithm reruns--an approach that is both time-consuming and does not guarantee satisfactory results. Therefore, for tasks where registration accuracy is more important than speed, it is appropriate to explore alternative, user-assisted registration strategies. In such tasks, finding and correcting errors in automated registration is often more time-consuming than directly integrating user input during the registration process. Therefore, this study evaluates a user-assisted approach for accurate nonrigid registration of images and traces. By leveraging the corresponding sets of fiducial points provided by the user to guide the registration, the algorithm computes an optimal nonrigid transformation that combines linear and nonlinear components. Our findings demonstrate that the registration accuracy of this approach improves consistently with the increased complexity of the linear transformation and as more fiducial points are provided. As a result, accuracy sufficient for many biomedical applications can be achieved within minutes, requiring only a small number of user-provided fiducial points.

bioengineering↗

bioRxiv2024

Normalized level set model for segmentation of low-contrast objects in 2- and 3- dimensional images

Analyses of biomedical images often rely on accurate segmentation of structures of interest. Traditional segmentation methods based on thresholding, watershed, fast marching, and level set perform well in high-contrast images containing structures of similar intensities. However, such methods can under-segment or miss entirely low-intensity objects on noisy backgrounds. Machine learning segmentation methods promise superior performance but require large training datasets of labeled images which are difficult to create, particularly in 3D. Here, we propose an algorithm based on the Local Binary Fitting (LBF) level set method, specifically designed to improve the segmentation of low-contrast structures.

bioengineering↗