Prof. Patrick J. Schuler Profile

Prof. Patrick J. Schuler

at Ulm Univ. Medical Ctr.

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Publications (2)

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Proceedings Article | 27 November 2023 Poster + Paper

Robust tissue differentiation in head and neck cancer: ex-vivo study with hyperspectral imaging and convolutional neural networks

Mayar Lotfy, Xiaohan Zhang, Christoph Hauger, Tommaso Giannantonio, Anna Alperovich, Felix Holm, Nassir Navab, Felix Boehm, Carolin Schwamborn, Thomas Hoffmann, Patrick Schuler

Proceedings Volume 12770, 127703M (2023) https://doi.org/10.1117/12.3005006

KEYWORDS: Tumors, Biological samples, Hyperspectral imaging, Neck, Head, RGB color model

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Tissue classification in surgical workflows between healthy and tumoral regions remains challenging both during and post-surgery. The current standard practice consists of taking small biopsies directly after tumor resection and sending them to pathologists for an intraoperative margin assessment, which is time-consuming and error prone due to the necessarily limited size and number of samples. Then, after the surgery is completed, the resected tumor is sent to the pathology lab, where its type and grading are further confirmed. The present workflow is prone to inaccuracies and particularly difficult when the sample is resected in several pieces. Therefore, an intraoperative tissue classification technology is highly sought-after for a simplified surgical workflow and better patient outcome. Our work aims at using hyperspectral images (HSI) for contact- and tracer-free tissue differentiation. We introduce a deep learning-based algorithm for the classification of tissue type that is based on spectral information and can be applied simultaneously to the whole sample. We illustrate the performance of our method on ex vivo head and neck squamous cell cancer samples. The proposed algorithm can differentiate between three main classes: background, tumor, and healthy tissues. Our experiments first assess the generalization of the neural network on data from unseen cases. We then determine the minimal number of training examples needed to cover the variety of tissue spectral appearances seen in the clinical dataset. We evaluate the influence of the delay between resection and start of image acquisition on the quality of the recorded HSI and the prediction. Qualitative and quantitative evaluations support the applicability of hyperspectral imaging for tissue classification and demonstrate an agreement between surgeon annotations and neural network predictions in most test cases.

Proceedings Article | 19 February 2020 Paper

Comparative in-vitro investigations on the cutting quality of the CO2 laser and the diode pumped Er:YAG laser

Karl Stock, Elisabeth Reins, Patrick Schuler, Holger Wurm

Proceedings Volume 11213, 112130D (2020) https://doi.org/10.1117/12.2545849

KEYWORDS: Laser cutting, Gas lasers, Er:YAG lasers, Carbon monoxide, Laser systems engineering, Diodes, Laser tissue interaction, Laser ablation, Carbon dioxide lasers, In vitro testing

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The CO₂laser has proved its worth in daily clinical use for soft tissue surgery because of the good cutting quality. Now DPSS Er:YAG laser systems are available, which promise a better cutting efficiency and minor thermal damages. Goal of this study was the comparison of both laser systems for soft tissue cutting.

Firstly, an experimental set-up was realized with a clinical CO₂ laser system with micromanipulator and focusing unit. The Er:YAG laser system was an experimental set-up (DPM40, Pantec Biosolutions AG) with focusing unit in order to achieve the same spot diameter (500 μm). For both, a computer-controlled translation stage with sample holder was used to move the sample (mucosa of fresh porcine tongues) with a defined velocity while irradiation by various laser parameters. Additionally, for the Er:YAG laser system, the influence of the laser power, cutting velocity, and pulse repetition rate on to the cut depth and thermal damage was examined. While irradiation the tissue effects were recorded by a video camera, adapted on a surgical microscope. After irradiation, the samples were analyzed by light microscopy. Also, histological sections were prepared and microscopically analyzed.

The Er:YAG laser shows higher cutting depth (about 1 mm (Er:YAG) compared to 500 μm (CO₂) at 7.7 W and 5 mm/s) and less coagulation damage (about 70 μm compared to 120 μm). Both the cutting depth and thermal damage zone can be adjusted in a wide range by varying the irradiation parameters.

In conclusion, these experiments demonstrate significant advantages of the diode pumped Er:YAG laser system for soft tissue cutting compared to the CO₂ laser, in particular it is more efficient and causes minor thermal damage.

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