Universal Soldier: A deep neural net for unsupervised 3D segmentation of tomographic images of bones

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The Universal Soldier Deep Net is an artificial convolutional neural net for image analysis. The Universal Soldier, or USDNet, will be the output of this collaborative project between Prof. Natalie Reznikov and Object Research Systems (ORS) Inc. (Montréal), whose product is the software Dragonfly for comprehensive 3D image analysis. The purpose is to design and train a deep artificial neural network (the Universal Soldier) that will be capable of unsupervised segmentation of 3D images of bones as acquired by various X-ray-based methods. Currently, image segmentation - i.e. the identification and accurate tagging of relevant features in 3D - is a bottleneck of bioimaging, largely because of the high degree of hierarchical complexity in biological objects (such as bones), together with the large footprint of accrued 3D data. Automated, unbiased segmentation of 3D datasets would abolish these limitations and increase the precision of quantitative image analysis, with high throughput. From 2020-22, we collected a vast library of 3D tomographic images of bones of various animals (including humans), acquired using X-ray computed tomography (CT) scanners, with resolutions ranging from 1 µm to 60 µm, and with a variety of naturally occurring artifacts. The library is currently structured as an SQL repository and contains about 2 TB of raw images, as well as expertly processed subsamples of raw data (training data, or "ground truth", about 5%). Having this library, as part of this proposed project we will now design and train the USDNet that will be able to recognize skeletal elements in any scan and produce unsupervised, high-fidelity automated segmentation. This Universal Soldier will become part of the image analysis software Dragonfly available to skeletal biologists and bioimaging researchers free of charge. This will popularize artificial intelligence-aided methodologies in the life sciences, will make quantitative 3D image analysis fast, streamlined and immune to cognitive biases. Like self-driving cars have become a reality today, automated segmentation using a pre-trained Universal Soldier Deep Net we believe will transform bioimaging tomorrow.