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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

22.03.2019 | Original Article

Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks

verfasst von: Tim J. Adler, Lynton Ardizzone, Anant Vemuri, Leonardo Ayala, Janek Gröhl, Thomas Kirchner, Sebastian Wirkert, Jakob Kruse, Carsten Rother, Ullrich Köthe, Lena Maier-Hein

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 6/2019

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Abstract

Purpose

Optical imaging is evolving as a key technique for advanced sensing in the operating room. Recent research has shown that machine learning algorithms can be used to address the inverse problem of converting pixel-wise multispectral reflectance measurements to underlying tissue parameters, such as oxygenation. Assessment of the specific hardware used in conjunction with such algorithms, however, has not properly addressed the possibility that the problem may be ill-posed.

Methods

We present a novel approach to the assessment of optical imaging modalities, which is sensitive to the different types of uncertainties that may occur when inferring tissue parameters. Based on the concept of invertible neural networks, our framework goes beyond point estimates and maps each multispectral measurement to a full posterior probability distribution which is capable of representing ambiguity in the solution via multiple modes. Performance metrics for a hardware setup can then be computed from the characteristics of the posteriors.

Results

Application of the assessment framework to the specific use case of camera selection for physiological parameter estimation yields the following insights: (1) estimation of tissue oxygenation from multispectral images is a well-posed problem, while (2) blood volume fraction may not be recovered without ambiguity. (3) In general, ambiguity may be reduced by increasing the number of spectral bands in the camera.

Conclusion

Our method could help to optimize optical camera design in an application-specific manner.

Vorheriger Artikel Estimation of tissue oxygen saturation from RGB images and sparse hyperspectral signals based on conditional generative adversarial network

Nächster Artikel Deep neural maps for unsupervised visualization of high-grade cancer in prostate biopsies

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Titel: Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
verfasst von: Tim J. Adler
Lynton Ardizzone
Anant Vemuri
Leonardo Ayala
Janek Gröhl
Thomas Kirchner
Sebastian Wirkert
Jakob Kruse
Carsten Rother
Ullrich Köthe
Lena Maier-Hein
Publikationsdatum: 22.03.2019
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 6/2019
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-01939-9

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Abstract

Purpose

Methods

Results

Conclusion

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