Efficient segmentation of multi-modal optoacoustic and ultrasound images using convolutional neural networks

Berkan Lafci, Elena Merćep, Stefan Morscher, Xosé Luís Deán-Ben, Daniel Razansky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


Multispectral optoacoustic tomography (MSOT) offers the unique capability to map the distribution of spectrally distinctive endogenous and exogenous substances in heterogeneous biological tissues by exciting the sample at various wavelengths and detecting the optoacoustically-induced ultrasound waves. This powerful functional and molecular imaging capability can greatly benefit from hybridization with pulse-echo ultrasound (US), which provides additional information on tissue anatomy and blood flow. However, speed of sound variations and acoustic mismatches in the imaged object generally lead to errors in the coregistration of compounded images and loss of spatial resolution in both imaging modalities. The spatially-and wavelength-dependent light fluence attenuation further limits the quantitative capabilities of MSOT. Proper segmentation of different regions and assignment of corresponding acoustic and optical properties turns then essential for maximizing the performance of hybrid optoacoustic and ultrasound (OPUS) imaging. Particularly, accurate segmentation of the boundary of the sample can significantly improve the images rendered. Herein, we propose an automatic segmentation method based on a convolutional neural network (CNN) for segmenting the mouse boundary in a pre-clinical OPUS system. The experimental performance of the method, as characterized with the Dice coefficient metric between the network output and the ground truth (manually segmented) images, is shown to be superior than that of a state-of-the-art active contour segmentation method in a series of two-dimensional (cross-sectional) OPUS images of the mouse brain, liver and kidney regions.

Original languageEnglish
Title of host publicationPhotons Plus Ultrasound
Subtitle of host publicationImaging and Sensing 2020
EditorsAlexander A. Oraevsky, Lihong V. Wang
ISBN (Electronic)9781510632431
StatePublished - 2020
Externally publishedYes
EventPhotons Plus Ultrasound: Imaging and Sensing 2020 - San Francisco, United States
Duration: 2 Feb 20205 Feb 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferencePhotons Plus Ultrasound: Imaging and Sensing 2020
Country/TerritoryUnited States
CitySan Francisco


  • Concave arrays
  • Deep Learning
  • Optoacoustic imaging
  • Segmentation
  • Ultrasound Imaging


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