COMBIOSCOPY: Computational biophotonics in endoscopic cancer diagnosis and therapy

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Replacing traditional open surgery with minimally-invasive interventions represents one of the most important challenges in modern healthcare. Minimally-invasive procedures provide numerous advantages over open surgery, including reduced surgical trauma, lesser need for pain medication, earlier convalescence, better cosmetic results, shorter hospitalization terms and lower costs. Furthermore, they are often the only promising treatment option for patients that are not eligible for surgery due to old age or poor overall medical condition, for example. Conventional medical imaging equipment in minimally-invasive procedures (e.g. endoscopes, laparoscopes), however, often offers poor tissue differentiation (e.g. healthy vs (pre)malignant or perfused vs not perfused), which results in inadequate treatment, long procedure times and high complication rates. Fusion of interventional imaging data with diagnostic data has shown promise to overcome some of these issues but suffers from the fact that tissue dynamics (e.g. hemodynamic changes resulting in ischemia) cannot be taken into account. Given these challenges, the goal of the COMBIOSCOPY project was to develop new safe and cost-effective concepts for interventional imaging that are particularly well-suited for supporting endoscopic interventions.

In the scope of the COMBIOSCOPY project, we developed new imaging concepts that (1) provide real-time discrimination of local tissue with a high contrast-to-noise-ratio, (2) are radiation-free to prevent the patient and staff from being exposed to harmful ionizing radiation and (3) feature a compact design at a low cost for a wide range of applicability and acceptance. Our methodology leverages recent spectral imaging techniques including multispectral optical and optoacoustic imaging as well as modern machine learning techniques to enable augmented reality visualization of a range of important morphological and functional parameters invisible to the naked eye. New methods for uncertainty analysis ensure high error awareness and robustness of the approach when applied in a clinical setting. According to a multistage validation process involving ongoing in-human studies, the methodology holds great potential for clinical translation.

• Dr. Alexander Seitel (Scientist)
• Niklas Holzwarth (Doctoral Student)
• Tim Adler (Doctoral Student)
• Leonardo Antonio Ayala Menjivar (Doctoral Student)
• Silvia Seidlitz (Doctoral Student)
• Melanie Schellenberg (Doctoral Student)
• Kris Dreher (Doctoral Student)
• Jan-Hinrich Nölke (Doctoral Student)
• Diana Mindroc-Filimon (alumni)
• Prof. Dr. Lena Maier-Hein (Principal Investigator)

• Dr. Janek Gröhl (Doctoral Student)
• Dr. Anant Vemuri (Scientist)
• Dr. Thomas Kirchner (Doctoral Student)
• Franz Sattler (Student Assistant)
• Dr. Sebastian Wirkert (Doctoral Student)
• Dominik Waibel (Master's student)
• Angelika Laha (Master's student)
• Dr. Sara Moccia (PhD intern)
• Yan Zhang (Master's student)
• Justin Iszatt (Bachelor's student)

Adler, T. J., Ardizzone, L., Ayala, L., Gröhl, J., Vemuri, A., Wirkert, S. J., Müller-Stich, B. P., Rother, C., Köthe, U., & Maier-Hein, L. (2019a). Uncertainty handling in intra-operative multispectral imaging with invertible neural networks. International Conference on Medical Imaging with Deep Learning. https://openreview.net/forum?id=Byx9RUONcE

Adler, T. J., Ardizzone, L., Vemuri, A., Ayala, L., Gröhl, J., Kirchner, T., Wirkert, S., Kruse, J., Rother, C., Köthe, U., & Maier-Hein, L. (2019b). Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks. International Journal of Computer Assisted Radiology and Surgery, 14(6), 997–1007. https://doi.org/10.1007/s11548-019-01939-9

Ardizzone, L., Kruse, J., Wirkert, S. J., Rahner, D., Pellegrini, E. W., Klessen, R. S., Maier-Hein, L., Rother, C., & Köthe, U. (2018). Analyzing Inverse Problems with Invertible Neural Networks. International Conference on Learning Representations. https://openreview.net/forum?id=rJed6j0cKX

Ayala, L. A., Wirkert, S. J., Gröhl, J., Herrera, M. A., Hernandez-Aguilera, A., Vemuri, A., Santos, E., & Maier-Hein, L. (2019a). Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis. OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging, 38–46. https://doi.org/10.1007/978-3-030-32695-1_5

Ayala, L., Wirkert, S., Herrera, M., Hernández-Aguilera, A., Vermuri, A., Santos, E., & Maier-Hein, L. (2019b). Abstract: Multispectral Imaging Enables Visualization of Spreading Depolarizations in Gyrencephalic Brain. Bildverarbeitung für die Medizin 2019, 244–244. https://doi.org/10.1007/978-3-658-25326-4_54

Ayala, L., Seidlitz, S., Vemuri, A., Wirkert, S. J., Kirchner, T., Adler, T. J., Engels, C., Teber, D., & Maier-Hein, L. (2020). Light source calibration for multispectral imaging in surgery. International Journal of Computer Assisted Radiology and Surgery, 15(7), 1117–1125. https://doi.org/10.1007/s11548-020-02195-y

Bohndiek, S., Brunker, J., Gröhl, J., Hacker, L., Joseph, J., Vogt, W. C., Armanetti, P., Assi, H., Bamber, J. C., Beard, P. C., & others. (2019). International Photoacoustic Standardisation Consortium (IPASC): Overview (Conference Presentation). Photons Plus Ultrasound: Imaging and Sensing 2019, 10878, 108781N.

Clancy, N. T., Jones, G., Maier-Hein, L., Elson, D. S., & Stoyanov, D. (2020). Surgical spectral imaging. Medical Image Analysis, 63, 101699. https://doi.org/10.1016/j.media.2020.101699

Gröhl, J., Kirchner, T., & Maier-Hein, L. (2017). Abstract: Quantitative Photoakustische Tomografie durch lokale Kontextkodierung. In Bildverarbeitung für die Medizin 2017 (pp. 153–153).

Gröhl, J., Kirchner, T., Adler, T., & Maier-Hein, L. (2018a). Confidence Estimation for Machine Learning-Based Quantitative Photoacoustics. Journal of Imaging, 4(12), 147. https://doi.org/10.3390/jimaging4120147

Gröhl, J., Kirchner, T., & Maier-Hein, L. (2018b). Confidence estimation for quantitative photoacoustic imaging. Photons Plus Ultrasound: Imaging and Sensing 2018, 10494, 104941C.

Gröhl, J. (2019a). International Photoacoustic Standardisation Consortium (IPASC): Recommendations for standardized data exchange in photoacoustic imaging (Conference Presentation). Photons Plus Ultrasound: Imaging and Sensing 2019, 10878, 108781S.

Gröhl, J., Kirchner, T., Adler, T., & Maier-Hein, L. (2019b). Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI). ArXiv Preprint ArXiv:1902.05839.

Gröhl, J., & Hacker, L. (2020a). International Photoacoustic Standardisation Consortium (IPASC): Progress in the data acquisition and management theme (Conference Presentation). Photons Plus Ultrasound: Imaging and Sensing 2020, 11240, 112401F.

Gröhl, J., Kirchner, T., Adler, T., & Maier-Hein, L. (2020b). Deep learning-based oxygenation estimation for multispectral photoacoustic imaging (Conference Presentation). Photons Plus Ultrasound: Imaging and Sensing 2020, 11240, 112402P.

Gröhl, J. (2020c). Data-driven quantitative photoacoustic imaging [PhD Thesis]. Universität Heidelberg.

Gröhl, J., Schellenberg, M., Dreher, K. K., & Maier-Hein, L. (2021a). Deep learning for biomedical photoacoustic imaging: A review—ScienceDirect. Photoacoustics. Retrieved March 1, 2021, from https://www.sciencedirect.com/science/article/pii/S2213597921000033

Gröhl, J., Schellenberg, M., Dreher, K. K., Holzwarth, N., Tizabi, M. D., Seitel, A., & Maier-Hein, L. (2021b). Semantic segmentation of multispectral photoacoustic images using deep learning. Photons Plus Ultrasound: Imaging and Sensing 2021 Conference Abstract.

Holzwarth, N., Schellenberg, M., Gröhl, J., Dreher, K. K., Seitel, A., Tizabi, M., Müller-Stich, B. P., & Maier-Hein, L. (2020). Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern. Retrieved March 1, 2021, from https://arxiv.org/abs/2011.04997

Kirchner, T., Wild, E., Maier-Hein, K. H., & Maier-Hein, L. (2016). Freehand photoacoustic tomography for 3D angiography using local gradient information. Photons Plus Ultrasound: Imaging and Sensing 2016, 9708, 97083G. https://doi.org/10.1117/12.2209368

Kirchner, T., Gröhl, J., & Maier-Hein, L. (2018a). Context encoding enables machine learning-based quantitative photoacoustics. Journal of Biomedical Optics, 23(5), 056008.

Kirchner, T., Gröhl, J., Sattler, F., Bischoff, M. S., Laha, A., Nolden, M., & Maier-Hein, L. (2018b). Real-time in vivo blood oxygenation measurements with an open-source software platform for translational photoacoustic research (Conference Presentation). Photons Plus Ultrasound: Imaging and Sensing 2018, 10494, 1049407. https://doi.org/10.1117/12.2288363

Kirchner, T., Sattler, F., Gröhl, J., & Maier-Hein, L. (2018c). Signed real-time delay multiply and sum beamforming for multispectral photoacoustic imaging. Journal of Imaging, 4(10), 121.

Kirchner, T., Gröhl, J., Herrera, M. A., Adler, T., Hernández-Aguilera, A., Santos, E., & Maier-Hein, L. (2019a). Photoacoustics can image spreading depolarization deep in gyrencephalic brain. Scientific Reports, 9(1), 1–9.

Kirchner, T., Gröhl, J., Holzwarth, N., Herrera, M. A., Hernández-Aguilera, A., Santos, E., & Maier-Hein, L. (2019b). Photoacoustic monitoring of blood oxygenation during neurosurgical interventions. Photons Plus Ultrasound: Imaging and Sensing 2019, 10878, 108780C.

Kirchner, T., Gröhl, J., Sattler, F., Bischoff, M. S., Laha, A., Nolden, M., & Maier-Hein, L. (2019c). An open-source software platform for translational photoacoustic research and its application to motion-corrected blood oxygenation estimation. ArXiv Preprint ArXiv:1901.09781.

Kirchner, T. (2019d). Real-time blood oxygenation tomography with multispectral photoacoustics [PhD Thesis]. Universität Heidelberg.

Lin, J., Clancy, N. T., Hu, Y., Qi, J., Tatla, T., Stoyanov, D., Maier-Hein, L., & Elson, D. S. (2017). Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging. In: Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017 (pp. 39–47). https://doi.org/10.1007/978-3-319-66185-8_5

Lin, J., Clancy, N. T., Qi, J., Hu, Y., Tatla, T., Stoyanov, D., Maier-Hein, L., & Elson, D. S. (2018). Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks. Medical Image Analysis, 48, 162–176. https://doi.org/10.1016/j.media.2018.06.004

Maier-Hein, L., Vedula, S. S., Speidel, S., Navab, N., Kikinis, R., Park, A., Eisenmann, M., Feussner, H., Forestier, G., Giannarou, S., Hashizume, M., Katic, D., Kenngott, H., Kranzfelder, M., Malpani, A., März, K., Neumuth, T., Padoy, N., Pugh, C., ... Jannin, P. (2017). Surgical data science for next-generation interventions. Nature Biomedical Engineering, 1(9), 691–696. https://doi.org/10.1038/s41551-017-0132-7

Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A. P., Carass, A., Feldmann, C., Frangi, A. F., Full, P. M., van Ginneken, B., Hanbury, A., Honauer, K., Kozubek, M., Landman, B. A., März, K., ... Kopp-Schneider, A. (2018). Why rankings of biomedical image analysis competitions should be interpreted with care. Nature Communications,9(1), 5217. https://doi.org/10.1038/s41467-018-07619-7

Moccia, S., Wirkert, S. J., Kenngott, H., Vemuri, A. S., Apitz, M., Mayer, B., De Momi, E., Mattos, L. S., & Maier-Hein, L. (2018). Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy. IEEE Transactions on Biomedical Engineering, 65(11), 2649–2659. https://doi.org/10.1109/TBME.2018.2813015

Nölke, J.-H., Adler, T. J., Gröhl, J., Ardizzone, L., Rother, C., Köthe, U., & Maier-Hein, L. (2020). Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging. Retrieved March 1, 2021, from https://arxiv.org/abs/2011.05110

Reinke, A., Eisenmann, M., Onogur, S., Stankovic, M., Scholz, P., Full, P. M., Bogunovic, H., Landman, B. A., Maier, O., Menze, B., Sharp, G. C., Sirinukunwattana, K., Speidel, S., van der Sommen, F., Zheng, G., Müller, H., Kozubek, M., Arbel, T., Bradley, A. P., ... Maier-Hein, L. (2018). How to Exploit Weaknesses in Biomedical Challenge Design and Organization. In A. F. Frangi, J. A. Schnabel, C. Davatzikos, C. Alberola-López, & G. Fichtinger (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (pp. 388–395). Springer International Publishing. https://doi.org/10.1007/978-3-030-00937-3_45

Sattler, F., Kirchner, T., Gröhl, J., & Maier-Hein, L. (2018). Real-time delay multiply and sum beamforming for multispectral photoacoustics (Conference Presentation). Photons Plus Ultrasound: Imaging and Sensing 2018, 10494, 104942Q. https://doi.org/10.1117/12.2285862

Vemuri, A. S., Wirkert, S. J., & Maier-Hein, L. (2018). Hyperspectral Camera Selection for Interventional Health-care. Retrieved March 1, 2021, from https://arxiv.org/abs/1904.02709

Waibel, D., Gröhl, J., Isensee, F., Kirchner, T., Maier-Hein, K., & Maier-Hein, L. (2018a). Reconstruction of initial pressure from limited view photoacoustic images using deep learning. Photons Plus Ultrasound: Imaging and Sensing 2018, 10494, 104942S. https://doi.org/10.1117/12.2288353

Waibel, D., Gröhl, J., Isensee, F., Maier-Hein, K. H., & Maier-Hein, L. (2018b). Abstract: Rekonstruktion der initialen Druckverteilung photoakustischer Bilder mit limitiertem Blickwinkel durch maschinelle Lernverfahren. In Bildverarbeitung für die Medizin 2018 (pp. 201–201).

Wirkert, Sebastian J., Clancy, N. T., Stoyanov, D., Arya, S., Hanna, G. B., Schlemmer, H.-P., Sauer, P., Elson, D. S., & Maier-Hein, L. (2014). Endoscopic Sheffield Index for Unsupervised In Vivo Spectral Band Selection. Computer-Assisted and Robotic Endoscopy, 110–120. https://doi.org/10.1007/978-3-319-13410-9_11

Wirkert, Sebastian J., Kenngott, H., Mayer, B., Mietkowski, P., Wagner, M., Sauer, P., Clancy, N. T., Elson, D. S., & Maier-Hein, L. (2016). Robust near real-time estimation of physiological parameters from megapixel multispectral images with inverse Monte Carlo and random forest regression. International Journal of Computer Assisted Radiology and Surgery, 11(6), 909–917. https://doi.org/10.1007/s11548-016-1376-5

Wirkert, Sebastian J., Vemuri, A. S., Kenngott, H. G., Moccia, S., Götz, M., Mayer, B. F. B., Maier-Hein, K. H., Elson, D. S., & Maier-Hein, L. (2017). Physiological Parameter Estimation from Multispectral Images Unleashed. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017, 134–141. https://doi.org/10.1007/978-3-319-66179-7_16

Wirkert, Sebastian J., Isensee, F., Vemuri, A. S., Maier-Hein, K., Fei, B., & Maier-Hein, L. (2018a). Domain and task specific multispectral band selection (Conference Presentation). Design and Quality for Biomedical Technologies XI, 10486, 104860H. https://doi.org/10.1117/12.2287824

Wirkert, Sebastian Josef. (2018b). Multispectral image analysis in laparoscopy – A machine learning approach to live perfusion monitoring [PhD Thesis] Karlsruher Institut für Technologie (KIT)

Wirkert, Sebastian J., Isensee, F., Vemuri, A. S., Ayala, L. A., Maier-Hein, K. H., Fei, B., & Maier-Hein, L. (2019). Task-specific multispectral band selection. ArXiv:1905.11297 [Physics]. http://arxiv.org/abs/1905.11297

Zhang, Y., Wirkert, S. J., Iszatt, J., Kenngott, H., Wagner, M., Mayer, B., Stock, C., Clancy, N. T., Elson, D. S., & Maier-Hein, L. (2016). Tissue classification for laparoscopic image understanding based on multispectral texture analysis. Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling,9786, 978619. https://doi.org/10.1117/12.2216090

Zhang, Y., Wirkert, S., Iszatt, J., Kenngott, H., Wagner, M., Mayer, B., Stock, C., Clancy, N. T., Elson, D. S., & Maier-Hein, L. (2017). Tissue classification for laparoscopic image understanding based on multispectral texture analysis. Journal of Medical Imaging, 4(1), 015001. https://doi.org/10.1117/1.JMI.4.1.015001

P1. Maier-Hein, L., Kirchner, T., Groehl, J., Machine learning-based quantitative photoacoustic tomography (PAT). European patent pending under no. EP16177204.1.

P2. Seidlitz, S., Vemuri, A., Wirkert, S., Ayala, L., Kirchner, T., Adler, T., Maier-Hein, L., Method and system for augmented imaging in open treatment using multispectral information. European patent pending under no. EP18186770.8

P3. Holzwarth N., Schellenberg M., Gröhl J., Maier-Hein L. Method and system for context-aware photoacoustic imaging. European patent pending under no. EP20193102.9

P4. Seidlitz, S., Vemuri, A., Wirkert, S., Ayala, L., Kirchner, T., Adler, T., Maier-Hein, L. Method and system for augmented imaging using multispectral information. European patent pending under no. EP18186700.3

Bench to Bedside Award at MICCAI workshop: OR2.0 Context-Aware Operating Theaters (2019)
L. Ayala et al. for his paper "Live Monitoring of Hemodynamic Changes with Multispectral Image Analysis"

SMIT Young Investigator Award (2019)
L. Ayala et al. for his paper "Deep Learning Approach to live Monitoring of Hemodynamic Changes with Multispectral Image Analysis"

First Prize Science Slam SMIT (2019)
K. Dreher and N. Holzwarth for their contribution about photoacoustic imaging

BVM Award (2019)
S. Wirkert for his doctoral thesis "Multispectral Image Analysis in Laparoscopy - A Machine Learning Approach"

Medical Image Analysis/MICCAI Best Paper Award (2018)
J. Lin, L. Maier-Hein, D. Elson, and co-authors for their paper "Dual-modality endoscopic probe for tissue surface shape renconstruction and hyperspectral imaging enabled by deep neural networks"

DKFZ PhD Retreat Best Poster Award (2018)
Janek Gröhl for is poster at the Annual PhD Retreat of the German Cancer Research Center

1st prize conhIT Nachwuchspreis (2017)
Janek Gröhl for his Master's thesis "Machine learning based quantitative photoacoustic tomography"

Thomas-Gessmann-Special-Award (2017)
Janek Gröhl for his Master's thesis "Machine learning based quantitative photoacoustic tomography"

Berlin-Brandenburg Academy Prize (2017)
L. Maier-Hein for outstanding achievements in cancer research

Best Pitch at Science Sparks Startups (2017)
S. Wirkert, A. Vemuri for their contribution "Rainbow Surgery"

Emil Salzer Prize (2016)
Lena Maier-Hein for her contributions to the fields of computer science, physics and medicine

Thomas-Gessmann Prize (2015)
Justin Iszatt for the Bachelor's thesis "Multispektrale Bildgebung in der Medizin - Entwicklung eines multispektralen Laparoskops zur Schätzung des Sauerstoffgehalts in Geweben"

Best Paper Award at the MICCAI CARE workshop (2014)
S. Wirkert et al. for his paper "Endoscopic Sheffield Index for Unsuspervised In Vivo Spectral Band Selection"

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