1. Results The algorithm is validated using artificial images of fibres. Statistical analysis showed no significant difference between the ground truth and the results of the algorithm. The two-photon microscopy data is analyzed using the algorithm. Visual inspection of the image data indicates that collagen does not always align in the direction of the strain (figure 3). Histograms of the orientation indicate that collagen becomes more aligned when strained. The variance in orientation does not alway increase with more applied strain. Unfortunately not enough data was avialable to obtain quantative results. Conclusions  3D principal curvature directions are an effective way to determine local orientation of tubular structures.  CED can be used to enhance collagen fibers in TPLSM images.  This study indicates that there is an increase in collagen alignment based on visual inspection of the orientation histograms. The variance however does not always correspond with the visual observations. References [1] Hoerstrup S.P. et al. Tissue Engineering of functional Trileaflet Heart Valves From Human Marrow Stromal Cells. Circulation, 106: 134-150. 2002. [2] Weickert J. Coherence-Enhancing Diffusion Filtering. Int. J. Comp. Vision, 31: 111-127, 1999. Quantification of Collagen Orientation in 3D Engineered Tissue Florie Daniels BioMedical Imaging and Modeling, BioMedical Image Analysis Introduction Tissue engineered heart valves are a promising alternative for current heart valve replacements. However, the mechanical properties of these valves are insufficient for implantation at the aortic position [1]. Collagen orientation is important to improve the mechanical properties of tissue engineered valves. Two-photon laser-scanning microscopy allows us to study the influence of strain on collagen orientation in 3D. An algorithm was designed for automatic orientation analysis. Methods Experimental setup Tissue engineered samples were prepared, which were unattached, attached (0% strain) and strained with 4% and 8% in a flexercell FX-4000T straining system. Two-photon imaging was performed using 800nm wavelength excitation of CNA35 labelled collagen. Image analysis The method used for automatic 3D orientation analysis is Principal Curvature Directions, which determines orientation locally. The principal curvatures and principal directions are determined using the Hessian matrix. The principal direction (eigenvector of Hessian) corresponding to the minimal principal curvature (eigenvalue of Hessian) points in the direction of the underlying structure. The Hessian consists of 2nd order Gaussian derivatives. To find the best fit between these derivatives and the underlying collagen fibers scale selection, based on the anisotropy of the eigenvalues of the Hessian is automated. Coherence-enhancing diffusion (CED) [2], which enhances the collagen fibers in the TPLSM images, was used as a preprocessing step to reduce the influence of noise. Figure 2: Part of a two-photon microscopy image. Left: original image. Right: image after coherence-enhancing diffusion Figure 1: 3D structure with its principal curvature directions Figure 3: Selected image slices (172 x 172 μm ) from TPLSM data. Top: Attached construct (0% strain). Bottom: Construct strained with 4%. TPLSM-dataMean orientation of θ (in degrees) Mean orientation of φ (in degrees) Variance in θ (in degrees 2 ) Variance in φ (in degrees 2 ) Experiment 1 E1 (unattached)46,890,031,95,4 A1 (0% strain)90,0 22,84,3 B1 (4% strain)90,090,130,411,7 Experiment 2 E2 (unattached)21,690,034,74,7 A2 (0%strain)90,193,634,37,0 B2 (4%strain)176,590,023,613,3 C2 (8% strain)169,290,222,67,8 Table 1: The mean and variance of collagen orientation Unattached Attached (0% strain) 4% strain Figure 4: Histograms of orientation angles. Left: Angle in xy-plane. Right: Angle from the z-axis