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

arXiv 2601.01315 ↗First posted 4 Jan 2026q-bio.TO cs.AI cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 68 references that are in the map. First posted on arXiv 4 Jan 2026.

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3 of 68 references.

References · 3

  1. 2021Deep DIC: Deep Learning-Based Digital Image Correlation for End-to-End Displacement and Strain Measurementcited by 8
  2. 2018Global Ultrasound Elastography Using Convolutional Neural Networkcited by 5
  3. 2024Quantifying Smooth Muscles Regional Organization in the Rat Bladder Using Immunohistochemistry, Multiphoton Microscopy and Machine Learningcited by 1

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