# Semantic segmentation of multispectral photoacoustic images using deep learning

arXiv: https://arxiv.org/abs/2105.09624 · HTML: https://mlsanity.com/paper/2105.09624/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A survey of loss functions for semantic segmentation](https://mlsanity.com/paper/2006.14822/) (2020)
- [Methods and open-source toolkit for analyzing and visualizing challenge results](https://mlsanity.com/paper/1910.05121/) (2019)
- [Common Limitations of Image Processing Metrics: A Picture Story](https://mlsanity.com/paper/2104.05642/) (2021)
- [Deep Learning in Photoacoustic Tomography: Current approaches and future directions](https://mlsanity.com/paper/2009.07608/) (2020)
- [Deep learning for biomedical photoacoustic imaging: A review](https://mlsanity.com/paper/2011.02744/) (2020)
- [Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)](https://mlsanity.com/paper/1902.05839/) (2019)
- [Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues](https://mlsanity.com/paper/1511.05846/) (2015)
- [A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images](https://mlsanity.com/paper/2007.05324/) (2020)
- [O-Net: A Convolutional Neural Network for Quantitative Photoacoustic Image Segmentation and Oximetry](https://mlsanity.com/paper/1911.01935/) (2019)
- [Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern](https://mlsanity.com/paper/2011.04997/) (2020)

## Built on by (most cited first, 4 of 4)

- [Unsupervised Domain Transfer with Conditional Invertible Neural Networks](https://mlsanity.com/paper/2303.10191/) (2023)
- [Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis](https://mlsanity.com/paper/2106.08445/) (2021)
- [OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing](https://mlsanity.com/paper/2206.08612/) (2022)
- [Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges](https://mlsanity.com/paper/2409.12977/) (2024)

## Abstract (arXiv)

Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic {and ultrasound} imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.