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Complex soft tissues, for example the knee meniscus, play a crucial role inmobility and joint health, but when damaged are incredibly difficult to repairand replace. This is due to their highly hierarchical and porous nature whichin turn leads to their unique mechanical properties. In order to design tissuesubstitutes, the internal architecture of the native tissue needs to beunderstood and replicated. Here we explore a combined audio-visual approach -so called transperceptual - to generate artificial architectures mimicking thenative ones. The proposed method uses both traditional imagery, and soundgenerated from each image as a method of rapidly comparing and contrasting theporosity and pore size within the samples. We have trained and tested agenerative adversarial network (GAN) on the 2D image stacks. The impact of thetraining set of images on the similarity of the artificial to the originaldataset was assessed by analyzing two samples. The first consisting of n=478pairs of audio and image files for which the images were downsampled to 64$\times$ 64 pixels, the second one consisting of n=7640 pairs of audio andimage files for which the full resolution 256 $\times$ 256 pixels is retainedbut each image is divided into 16 squares to maintain the limit of 64 $\times$64 pixels required by the GAN. We reconstruct the 2D stacks of artificiallygenerated datasets into 3D objects and run image analysis algorithms tocharacterize statistically the architectural parameters - pore size, tortuosityand pore connectivity - and compare them with the original dataset. Resultsshow that the artificially generated dataset that undergoes downsamplingperforms better in terms of parameter matching. Our audiovisual approach hasthe potential to be extended to larger data sets to explore both howsimilarities and differences can be audibly recognized across multiple samples.

The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues