Methodology and tools for creating training samples for artificial intelligence systems for recognizing lung cancer on CT images

Abstract

Introduction. Medical imaging techniques can diagnose many diseases at the early stages of their development, improving the patient survival. Artificial intelligence (AI) systems, requiring the high-quality annotated and marked-up sets of medical images, are a suitable and promising means of improving the diagnostics' quality. The purpose of the study was to develop a methodology and software for creating AIS training sets. Material and methods. We compared the main annotation methods' performance and accuracy and based the information system on the most efficient method in both domains to develop an optimal approach. To markup objects of interest, we used the cluster model of lesions localization previously developed by the authors. We used C++ and Kotlin programming languages for software development. Results. A structured annotation template with delivered a glossary of terms became the basis of the information system. The latter consists of three interacting modules, two of which are executed on a remote server's capacities and one on a personal computer or mobile device of the end-user. The first module is a web service responsible for the workflow logic. The second module, a web server, is responsible for interacting with client applications. Its role is to identify users and manage the database and Picture Archiving and Communication System (PACS) connections. The front-end module is a web application with a graphical interface that assists the end-user in images' markup and annotation. Conclusions. An algorithmic basis and a software package have been created for annotation and markup of CT images. The resulting information system was used in a large-scale lung cancer screening project for the creation of medical imaging datasets.