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

Universal Soldier：用于骨骼断层图像无监督 3D 分割的深度神经网络

• 批准号: 576736-2022
• 负责人: Reznikov, NatalieN
• 金额: $ 2.19万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=744441
• 关键词: Universal; Soldier; deep; neural; net

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.

通用士兵深网是用于图像分析的人工卷积神经网。环球士兵或USDNET将是Natalie Reznikov教授和对象研究系统（ORS）Inc.（Montréal）之间的合作项目的输出，其产品是用于综合3D图像分析的软件。目的是设计和训练一个深人造神经网络（通用士兵），该网络将能够无监督的分割对各种基于X射线的方法获得的骨骼的3D图像。当前，图像分割 - 即3D中相关特征的识别和准确标记 - 是生物成像的瓶颈，主要是因为生物学对象（例如骨骼）的层次层次复杂性高度，以及累积的3D数据的巨大脚印。 3D数据集的自动，公正的分割将消除这些局限性，并以高吞吐量的高度提高定量图像分析的精度。从2020 - 22年开始，我们收集了使用X射线计算机断层扫描（CT）扫描仪获得的各种动物骨骼（包括人）的大量3D层析成像图像，其分辨率范围从1 µm到60 µm，并且具有多种自然发生的工件。该库目前以SQL存储库的形式构建，并包含大约2 TB的原始图像，以及经过精心处理的原始数据的子样本（训练数据或“地面真相”，约为5％）。拥有该库，作为该拟议项目的一部分，我们现在将设计和训练USDNET，该项目将能够识别任何扫描中的骨骼元素，并产生无监督，高保真的自动化段。这位通用士兵将免费成为骨骼生物学家和生物成像研究人员免费的图像分析软件的一部分。这将在生命科学中普及人工智能方法，将使定量的3D图像分析快速，简化和免疫认知偏见。就像自动驾驶汽车在今天成为现实一样，使用预训练的通用士兵深网的自动分割我们认为明天将改变生物成像。