A Partially-Learned Algorithm for Joint Photo-acoustic Reconstruction and Segmentation

Yoeri E. Boink*, Srirang Manohar, Christoph Brune

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Scopus)
1 Downloads (Pure)


In an inhomogeneously illuminated photoacoustic image, important information like vascular geometry is not readily available, when only the initial pressure is reconstructed. To obtain the desired information, algorithms for image segmentation are often applied as a post-processing step. In this article, we propose to jointly acquire the photoacoustic reconstruction and segmentation, by modifying a recently developed partially learned algorithm based on a convolutional neural network. We investigate the stability of the algorithm against changes in initial pressures and photoacoustic system settings. These insights are used to develop an algorithm that is robust to input and system settings. Our approach can easily be applied to other imaging modalities and can be modified to perform other high-level tasks different from segmentation. The method is validated on challenging synthetic and experimental photoacoustic tomography data in limited angle and limited view scenarios. It is computationally less expensive than classical iterative methods and enables higher quality reconstructions and segmentations than the state-of-the-art learned and non-learned methods.

Original languageEnglish
Article number8734097
Pages (from-to)129-139
Number of pages11
JournalIEEE transactions on medical imaging
Issue number1
Publication statusPublished - 10 Jun 2019


  • 22/2 OA procedure
  • Inverse problems
  • Convolutional neural networks (CNN)
  • Photoacoustic tomography
  • Segmentation
  • Learned iterative reconstruction


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