Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors

TL;DR

This paper explores the use of active contour models for boundary segmentation in optoacoustic tomography to improve image reconstruction accuracy by accounting for tissue heterogeneities.

Contribution

It introduces a novel segmentation approach using active contours to enhance optoacoustic image reconstruction by modeling tissue heterogeneities.

Findings

Segmentation improves reconstruction accuracy in tissue-mimicking phantoms.

Active contour models effectively delineate tissue boundaries in low-contrast images.

The method enhances small animal imaging quality.

Abstract

Segmentation of biomedical images is essential for studying and characterizing anatomical structures, detection and evaluation of pathological tissues. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities of the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.