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.

Universal Soldier Deep Net是一种用于图像分析的人工卷积神经网络。Universal Soldier或USDNet将是Natalie Reznikov教授和Object Research Systems（ORS）Inc.之间合作项目的成果。（蒙特利尔），其产品是用于全面3D图像分析的软件Dragonfly。其目的是设计和训练一个深度人工神经网络（通用士兵），该网络将能够对通过各种基于X射线的方法获得的骨骼3D图像进行无监督分割。 目前，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，这主要是因为生物对象（例如骨骼）的高度层次复杂性以及累积的3D数据的大足迹。 3D数据集的自动无偏分割将消除这些限制，并以高通量提高定量图像分析的精度。从2020年至2022年，我们收集了大量使用X射线计算机断层扫描（CT）扫描仪获取的各种动物（包括人类）骨骼的三维断层扫描图像库，分辨率从1 µm到60 µm不等，并带有各种自然产生的伪影。 该库目前的结构是一个SQL存储库，包含大约2 TB的原始图像，以及经过专业处理的原始数据子样本（训练数据，或“地面实况”，约5%）。 有了这个库，作为这个拟议项目的一部分，我们现在将设计和训练USDNet，它将能够在任何扫描中识别骨骼元素，并产生无监督的高保真自动分割。 这个通用士兵将成为图像分析软件Dragonfly的一部分，免费提供给骨骼生物学家和生物成像研究人员。 这将在生命科学中推广人工智能辅助方法，使定量3D图像分析快速，简化并免受认知偏见的影响。就像自动驾驶汽车今天已经成为现实一样，使用预先训练的通用士兵深度网络进行自动分割，我们相信明天将改变生物成像。