Medical instrument detection with synthetically generated data

L. Wiese; L. Hinz; E. Reithmeier

doi:10.1117/12.3005798

2 April 2024 Medical instrument detection with synthetically generated data

L. Wiese, L. Hinz, E. Reithmeier

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 1293109 (2024) https://doi.org/10.1117/12.3005798
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

The persistent need for more qualified personnel in operating theatres exacerbates the remaining staff’s workload. This increased burden can result in substantial complications during surgical procedures. To address this issue, this research project works on a comprehensive operating theatre system. The system offers real-time monitoring of all surgical instruments in the operating theatre, aiming to alleviate the problem. The foundation of this endeavor involves a neural network trained to classify and identify eight distinct instruments belonging to four distinct surgical instrument groups. A novel aspect of this study lies in the approach taken to select and generate the training and validation data sets. The data sets used in this study consist of synthetically generated image data rather than real image data. Additionally, three virtual scenes were designed to serve as the background for a generation algorithm. This algorithm randomly positions the instruments within these scenes, producing annotated rendered RGB images of the generated scenes. To assess the efficacy of this approach, a separate real data set was also created for testing the neural network. Surprisingly, it was discovered that neural networks trained solely on synthetic data performed well when applied to real data. This research paper shows that it is possible to train neural networks with purely synthetically generated data and use them to recognize surgical instruments in real images.

Conference Presentation

Citation Download Citation

L. Wiese, L. Hinz, and E. Reithmeier "Medical instrument detection with synthetically generated data", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 1293109 (2 April 2024); https://doi.org/10.1117/12.3005798

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