Quantifying Local Strain Field and Deformation in Active Contraction of Bladder Using a Pretrained Transformer Model: A Speckle-Free Approach

Accurate quantification of local strain fields during bladder contraction is essential for understanding the biomechanics of bladder micturition, in both health and disease. Conventional digital image correlation (DIC) methods have been successfully applied to various biological tissues; however, this approach requires artificial speckling, which can alter both passive and active properties of the tissue. In this study, we introduce a speckle-free framework for quantifying local strain fields using a state-of-the-art, zero-shot transformer model, CoTracker3. We utilized a custom-designed, portable isotonic biaxial apparatus compatible with multiphoton microscopy (MPM) to demonstrate this approach, successfully tracking natural bladder lumen textures without artificial markers. Benchmark tests validated the method's high pixel accuracy and low strain errors. Our framework effectively captured heterogeneous deformation patterns, despite complex folding and buckling, which conventional DIC often fails to track. Application to in vitro active bladder contractions in four rat specimens (n=4) revealed statistically significant anisotropy (p<0.01), with higher contraction longitudinally compared to circumferentially. Multiphoton microscopy further illustrated and confirmed heterogeneous morphological changes, such as large fold formation during active contraction. This non-invasive approach eliminates speckle-induced artifacts, enabling more physiologically relevant measurements, and has broad applicability for material testing of other biological and engineered systems.

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.

Phenotyping calcification in vascular tissues using artificial intelligence

Vascular calcification is implicated as an important factor in major adverse cardiovascular events (MACE), including heart attack and stroke. A controversy remains over how to integrate the diverse forms of vascular calcification into clinical risk assessment tools. Even the commonly used calcium score for coronary arteries, which assumes risk scales positively with total calcification, has important inconsistencies. Fundamental studies are needed to determine how risk is influenced by the diverse calcification phenotypes. However, studies of these kinds are hindered by the lack of high-throughput, objective, and non-destructive tools for classifying calcification in imaging data sets. Here, we introduce a new classification system for phenotyping calcification along with a semi-automated, non-destructive pipeline that can distinguish these phenotypes in even atherosclerotic tissues. The pipeline includes a deep-learning-based framework for segmenting lipid pools in noisy micro-CT images and an unsupervised clustering framework for categorizing calcification based on size, clustering, and topology. This approach is illustrated for five vascular specimens, providing phenotyping for thousands of calcification particles across as many as 3200 images in less than seven hours. Average Dice Similarity Coefficients of 0.96 and 0.87 could be achieved for tissue and lipid pool, respectively, with training and validation needed on only 13 images despite the high heterogeneity in these tissues. By introducing an efficient and comprehensive approach to phenotyping calcification, this work enables large-scale studies to identify a more reliable indicator of the risk of cardiovascular events, a leading cause of global mortality and morbidity.