# A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation

## TL;DR

This paper introduces a partially learned neural network algorithm that jointly reconstructs and segments photoacoustic images, improving robustness, quality, and computational efficiency over traditional methods.

## Contribution

It develops a novel joint reconstruction and segmentation method using a partially learned neural network, enhancing robustness and quality in photoacoustic imaging.

## Key findings

- Outperforms classical iterative methods in quality and speed.
- Robust against variations in initial pressure and system settings.
- Validated on synthetic and experimental data in limited view scenarios.

## Abstract

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 work, 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 state-of-the-art learned and non-learned methods.