基于深度学习的工作流程，用于重建和分割具有挑战性的时间分辨 X 射线显微计算机断层扫描数据集

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2024-07-13 DOI:10.1016/j.softx.2024.101796

Samuel Waldner, Jörg Huwyler, Maxim Puchkov

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引用次数: 0

摘要

我们提出了一种基于深度学习的软件管道，用于重建和分割大量时间分辨微型计算机断层扫描（µCT）图像数据集。我们构建并训练了一个卷积神经网络（CNN），以持续、快速、自主地分割时间分辨断层扫描数据。我们对前面的 CT 重建步骤进行了参数化处理，以获得最佳的分割图像质量。我们演示了如何在不同介质（如空气和水）存在的情况下分辨具有相似射线密度的材料。我们的方法既可用于类似的 µCT 数据，也可通过重新训练神经网络适用于任何类似的具有挑战性的三维图像数据。

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A deep-learning-based workflow for reconstructing and segmenting challenging sets of time-resolved X-ray micro-computed tomography data

We present a deep-learning-based software pipeline for reconstructing and segmenting large sets of time-resolved micro-computed tomography (µCT) image data. We construct and train a convolutional neural network (CNN) to consistently, rapidly, and autonomously segment the time-resolved tomography data. The preceding CT reconstruction steps are parametrized for optimal image quality for segmentation. We demonstrate how to discriminate materials with similar radiographic densities in the presence of different media, such as air and water. Our approach can be used out of the box for similar µCT data or adapted to any similarly challenging 3D image data by retraining the neural network.

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.