Mr. Leon Wiese Profile

Leon Wiese

at Leibniz Univ. Hannover

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Publications (2)

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Proceedings Article | 2 April 2024 Presentation + Paper

Medical instrument detection with synthetically generated data

L. Wiese, L. Hinz, E. Reithmeier

Proceedings Volume 12931, 1293109 (2024) https://doi.org/10.1117/12.3005798

KEYWORDS: Equipment, Education and training, 3D modeling, Neural networks, Data modeling, Image segmentation, Surgery, Mathematical optimization, Instrument modeling, Image processing

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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.

Proceedings Article | 3 October 2022 Presentation + Paper

Pose estimation of optical resonators using convolutional neural networks in a simulation environment

Nils Melchert, Kolja Hedrich, Leon Wiese, Lennart Hinz, Eduard Reithmeier

Proceedings Volume 12222, 122220D (2022) https://doi.org/10.1117/12.2633416

KEYWORDS: Resonators, Optical resonators, Fabry–Perot interferometers, Monte Carlo methods, Convolutional neural networks, Network architectures, Optical design, Mirrors, Computer programming, Neural networks

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External Fabry-Perot resonators are widely used in the field of optics and are well established in areas such as frequency selection and spectroscopy. However, fine tuning and thus most efficient coupling of these resonators into the optical path is a time-consuming task, which is usually performed manually by trained personnel. The state of the art includes many different approaches for automatic alignment, which, however, are designed for special optical configurations and cannot be generalized. However, these approaches are only valid for individually designed optical systems and are not universally applicable. Moreover, none of these approaches address the identification of the spatial degrees of freedom of the resonator. Knowledge of this exact pose information can generally be integrated into the alignment process and has great potential for automation. In this work, convolutional neural networks (CNNs) are applied to identify the sensitive spatial degrees of freedom of a FabryPerot resonator in a simulation environment. For this purpose, well established CNN architectures, which are typically used for feature extraction, are adapted to this regression problem. The input of the CNNs was chosen to be the intensity profiles of the transversal modes, which can be obtained from the transmitted power behind the resonator. These modes are known to be highly correlated with the coupling quality and thus with the spatial location of resonators. To achieve an exact pose estimation, the CNN input consists of several images of mode profiles, which are propagated through an encoder structure followed by fully-connected layers providing the four spatial parameters as the network output. For training and evaluation, intensity images as well as resonator poses are obtained from a simulation of a free spectral range of a resonator. Finally, different encoder structures including a memory efficient, small self-developed network architecture are evaluated.

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