Jialing Yu
ABSTRACT
Optical-resolution photoacoustic microcopy (OR-PAM) is a non-invasive imaging technology that can achieve high-resolution vascular imaging and has been widely used in the field of vessel segmentation. Due to the low contrast of photoacoustic microvessel images and the discontinuity of vessels, existing vessel segmentation methods have some shortcomings: the accuracy of capillary extraction is insufficient, it is difficult to extract thick and thin vessels at the same time, and the vessels have deformed in some segmentation results. Therefore, we propose a deep learning network (RA-MP-Net) that integrates the residual attention(RA) module and the multi-scale perception (MP) module. Among them, RA module can focus on the vascular region in the image, and MP module can aggregate multi-scale vessel features. We conduct experiments on the normal vessel dataset from OR-PAM. Compared with existing work, the proposed microvascular segmentation algorithm can extract thick and thin vessels more accurately and continuously. In quantitative analysis, compared with the previously proposed 2D deep learning method, our evaluation indicators improved by 2.4%∽10.6%. The improvement in the accuracy of extracting vessels using our method is helpful for the diagnosis and evaluation of microvascular diseases.
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Index Terms
- RA-MP-Net: Residual attention and multi-scale perception network for microvasculature extraction in optical-resolution photoacoustic microscopy
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