# A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation

arXiv: https://arxiv.org/abs/1906.07499 · HTML: https://mlsanity.com/paper/1906.07499/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 10 of 10)

- [Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography](https://mlsanity.com/paper/2304.01963/) (2023)
- [Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography](https://mlsanity.com/paper/2204.06270/) (2022)
- [Digital twins enable full-reference quality assessment of photoacoustic image reconstructions](https://mlsanity.com/paper/2505.24514/) (2025)
- [On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector](https://mlsanity.com/paper/2204.10001/) (2022)
- [Deep Learning for Material Decomposition in Photon-Counting CT](https://mlsanity.com/paper/2208.03360/) (2022)
- [Learned Interferometric Imaging for the SPIDER Instrument](https://mlsanity.com/paper/2301.10260/) (2023)
- [Deep Learning Adapted Acceleration for Limited-view Photoacoustic Computed Tomography](https://mlsanity.com/paper/2111.05194/) (2021)
- [Cross-domain Self-supervised Framework for Photoacoustic Computed Tomography Image Reconstruction](https://mlsanity.com/paper/2301.06681/) (2023)
- [Variational multichannel multiclass segmentation using unsupervised lifting with CNNs](https://mlsanity.com/paper/2302.02214/) (2023)
- [Learning end-to-end inversion of circular Radon transforms in the partial radial setup](https://mlsanity.com/paper/2308.14144/) (2023)

## Abstract (arXiv)

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.