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While this approach has many relevant clinical applications, it suffers from one core bottleneck: it cannot account for tissue dynamics because it works with \u201coffline\u201d 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 <jats:italic>in silico<\/jats:italic> 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 <jats:italic>in vivo<\/jats:italic>. Deep learning based multispectral imaging could thus become a valuable tool for imaging tissue dynamics.<\/jats:p>","DOI":"10.1007\/978-3-030-32695-1_5","type":"book-chapter","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T17:10:36Z","timestamp":1570727436000},"page":"38-46","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Live Monitoring of Haemodynamic Changes with Multispectral Image\u00a0Analysis"],"prefix":"10.1007","author":[{"given":"Leonardo A.","family":"Ayala","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian J.","family":"Wirkert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janek","family":"Gr\u00f6hl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mildred A.","family":"Herrera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Hernandez-Aguilera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anant","family":"Vemuri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edgar","family":"Santos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lena","family":"Maier-Hein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,10,7]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1007\/s11548-019-01939-9","volume":"14","author":"TJ Adler","year":"2019","unstructured":"Adler, T.J., et al.: Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks. 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