Preparing CT imaging datasets for deep learning in lung nodule analysis:Insights from four well-known datasets

Background: Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering.Methods: The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order.Findings: The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided.Discussion and conclusion: Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.</p

Electrically conducting fibres for e-textiles: An open playground for conjugated polymers and carbon nanomaterials

Conducting fibres and yarns promise to become an essential part of the next generation of wearable electronics that seamlessly integrate electronic function into one of the most versatile and most widely used form of materials: textiles. This review explores the many types of conducting fibres and yarns that can be realised with conjugated polymers and carbon materials, including carbon black, carbon nanotubes and graphene. We discuss how the interplay of materials properties and the chosen processing technique lead to fibres with a wide range of electrical and mechanical properties. Depending on the choice of conjugated polymer, carbon nanotube, graphene, polymer blend, or nanocomposite the electrical conductivity can vary from less than 10−3 to more than 103 S cm−1, accompanied by an increase in Young's modulus from 10 s of MPa to 100 s of GPa. Further, we discuss how conducting fibres can be integrated into electronic textiles (e-textiles) through e.g. weaving and knitting. Then, we provide an overview of some of the envisaged functionalities, such as sensing, data processing and storage, as well as energy harvesting e.g. by using the piezoelectric, thermoelectric, triboelectric or photovoltaic effect. Finally, we critically discuss sustainability aspects such as the supply of materials, their toxicity, the embodied energy of fibre and textile production and recyclability, which currently are not adequately considered but must be taken into account to ready carbon based conducting fibres for truly practical e-textile applications.Accepted author manuscriptDesign for Sustainabilit