Abstract
State-of-the-art concepts in the field of computer assisted medical interventions are typically based on registering pre-operative imaging data to the patient. While this approach has many relevant clinical applications, it suffers from one core bottleneck: it cannot account for tissue dynamics because it works with “offline” data. To overcome this issue, we propose a new approach to surgical imaging that combines the power of multispectral imaging with the speed and robustness of deep learning based image analysis. Core innovation is an end-to-end deep learning architecture that integrates all preprocessing steps as well as the actual regression task in a single network. According to a quantitative in silico validation, our approach is well-suited for solving the inverse problem of relating multispectral image pixels to underlying functional tissue properties in real time. A porcine study further suggests that our method is capable of monitoring haemodynamic changes in vivo. Deep learning based multispectral imaging could thus become a valuable tool for imaging tissue dynamics.
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References
Adler, T.J., et al.: Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks. Int. J. Comput. Assist. Radiol. Surg. 14, 997–1007 (2019)
Alerstam, E., Lo, W., David, T.D., Rose, J., Andersson-Engels, S., Lilge, L.: Next-generation acceleration and code optimization for light transport in turbid media using GPUs. Biomed. Opt. Express 1, 658–675 (2010)
Arganda-Carreras, I., Sorzano, C.O.S., Marabini, R., Carazo, J.M., Ortiz-de-Solorzano, C., Kybic, J.: Consistent and elastic registration of histological sections using vector-spline regularization. In: Beichel, R.R., Sonka, M. (eds.) CVAMIA 2006. LNCS, vol. 4241, pp. 85–95. Springer, Heidelberg (2006). https://doi.org/10.1007/11889762_8
Dreier, J.P., Reiffurth, C.: The stroke-migraine depolarization continuum. Neuron 86, 902–922 (2015)
Kirchner, T., et al.: Photoacoustics can image spreading depolarization deep in gyrencephalic brain. Sci. Rep. 9, 8661 (2019)
Moccia, S., et al.: Uncertainty-aware organ classification for surgical data science applications in laparoscopy. IEEE Trans. Biomed. Eng. 65, 2649–2659 (2018)
Rueden, C.T., et al.: Image J2: ImageJ for the next generation of scientific image data. BMC Bioinform. 18, 529 (2017)
Santos, E., et al.: Cortical spreading depression dynamics can be studied using intrinsic optical signal imaging in gyrencephalic animal cortex. In: Katayama, Y., Maeda, T., Kuroiwa, T. (eds.) Brain Edema XV. NEUROCHIRURGICA, vol. 118. Springer, Vienna (2013). https://doi.org/10.1007/978-3-7091-1434-6_16
Santos, E., et al.: Radial, spiral and reverberating waves of spreading depolarization occur in the gyrencephalic brain. NeuroImage 99, 244–255 (2014)
Sassaroli, A., Fantini, S.: Comment on the modified Beer-Lambert law for scattering media. Phys. Med. Biol. 49, N255–N257 (2004)
Schöll, M.J., Santos, E., et al.: Large field-of-view movement-compensated intrinsic optical signal imaging for the characterization of the haemodynamic response to spreading depolarizations in large gyrencephalic brains. J. Cereb. Blood Flow Metab. 37, 1706–1719 (2017)
Takano, T., et al.: Cortical spreading depression causes and coincides with tissue hypoxia. Nat. Neurosci. 10, 754–762 (2007)
Wirkert, S.J., et al.: Endoscopic sheffield index for unsupervised In Vivo spectral band selection. In: Luo, X., Reichl, T., Mirota, D., Soper, T. (eds.) CARE 2014. LNCS, vol. 8899, pp. 110–120. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13410-9_11
Wirkert, S.J., et al.: Robust near real-time estimation of physiological parameters from megapixel multispectral images with inverse Monte Carlo and random forest regression. Int. J. Comput. Assist. Radiol. Surg. 11, 909–917 (2016)
Wirkert, S.J., et al.: Physiological parameter estimation from multispectral images unleashed. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 134–141. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_16
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Ayala, L.A. et al. (2019). Live Monitoring of Haemodynamic Changes with Multispectral Image Analysis. In: Zhou, L., et al. OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. OR 2.0 MLCN 2019 2019. Lecture Notes in Computer Science(), vol 11796. Springer, Cham. https://doi.org/10.1007/978-3-030-32695-1_5
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DOI: https://doi.org/10.1007/978-3-030-32695-1_5
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