SOPHYSM: More Steps towards Digital Twins of Solid Tumours

The notion of digital twin is well established in several engineering disciplines, but, due to the complexity of the field, it is still in the making when it comes to the Life Sciences. We are addressing this issue, while realizing that many different pieces must come together to progress toward the goal. The SOPHYSM system is a Julia tool-set for simulating the evolution of solid tumours, while tracking genotyping information at the Single-cell level. To be more useful for Life Science practitioners, SOPHYSM provides a histopathology image handling facility. The images are analysed to pinpoint cells in a tissue. The cell positions and their morphological boundaries are then fed into a spatial simulator as a basis to compute possible solid tumour evolutions. In this paper we describe the useful (and necessary) porting to Julia of one of the latest image analysis “algorithms” for cell identification: the Python-based Cellpose. This allowed us to simplify our development pipeline for identifying (i.e., segment) cells’ positions from histopathology images. Here we report on this effort, which required unpacking and reverse engineering what is a sophisticated AI tool, Cellpose, to port its public weights to our Julia implementation, by leveraging the ONNX libraries. It should be noted that this is an example of the importance of providing the weights of a trained AI model, to be able to reuse them in different settings, like, in our case, SOPHYSM.