# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 1 Introduction  
With the continuous development of medical imaging technology, photoacoustic imaging (PAI) has attracted more and more attention as a fast-developing hybrid biomedical imaging technology [1; 2; 3]. PAI combines the advantages of optical imaging and ultrasound imaging, and has unique advantages in imaging depth, spatial resolution, and tissue imaging. It is able to provide abundant tissue functional and structural information, widely used in biomedical fields [4; 5; 6]. In a variety of clinical applications, PAI has shown significant potential and effectiveness [7]. For example, breast cancer screening and diagnosis [8; 9]: early lesions and malignant tumors have been identified by detecting blood oxygen saturation and angiogenesis in breast tissue; diagnosis of skin lesions [10; 11]: skin cancer and melanoma have been identified by detecting the concentration of melanin and vascular structure in the skin; cardiovascular disease monitoring [12; 13]: the severity of atherosclerosis has been assessed by real-time imaging of intravascular lipid cores and calcified plaques; tumor detection [14; 15]: by detecting blood flow changes and blood oxygen levels in tissues, important information is provided for early detection and surgical navigation. However, PAI still has limitations in practical applications. Non-ideal signal detection can significantly reduce the image quality of PAI [16]. In addition, the generation of PAI images relies on acoustic waves generated from biological tissues, which can only be sampledin the spatial dimension. Each discrete spatial measurement requires its own detector, and it may be infeasible to build an imaging system with a sufficiently large number of detectors due to practical and physical limitations [17]. Reconstruction of sampled data using standard methods results in low-quality images with serious detail loss. This has become a major obstacle to the clinical promotion of PAI. Therefore, despite the great progress in PAI, there are still great challenges in image quality improvement and accurate segmentation of tissue structures [18; 19]. Traditional photoacoustic image reconstruction and segmentation methods often rely on hand-designed feature extractors and mathematical models, which have many limitations in dealing with complex backgrounds, noise interference, and blurred organizational boundaries [20; 21]. For example, filter- or edge detector-based methods have limited effectiveness in dealing with low-contrast structures and subtle features in PAI, while threshold-based segmentation methods are sensitive to noise and artifacts and are prone to producing erroneous segmentation results. This restricts their advancement in real-world uses. In order to overcome the limitations of traditional methods, deep learning-based image reconstruction and segmentation methods have gradually attracted the attention of researchers in recent years [22; 23; 24; 25]. Currently, deep-learning techniques have been widely used in the field of optoacoustic imaging to acquire high-quality optoacoustic images through large-scale learning and to use the reconstructed optoacoustic images for disease segmentation, classification, and detection. With their strong feature learning capabilities and end-to-end data-driven advantages, deep-learning models can effectively and accurately perform reconstruction and segmentation tasks using optoacoustic image data, as well as develop complicated representations. Deep learning-based techniques have superior generalization and robustness over traditional techniques for handling noise, artifacts, and structural complexity in PAI [26; 27; 28]. However, despite the significant progress made by deep-learning methods in the field of PAI processing, there are still some challenges and limitations.