Ocular neovascularization occurs in various eye diseases such as diabetic retinopathy, neovascular macular degeneration, and retinopathy of prematurity. Current treatment methods including conventional laser ablation therapy and anti-vascular endothelial growth factor (VEGF) injection each has drawbacks including collateral tissue damages, frequent administration, high cost, and drug toxicity. We recently developed a novel noninvasive image-guided photo-mediated ultrasound therapy (PUT) which concurrently applies nanosecond laser pulses and millisecond ultrasound bursts to precisely and safely remove pathologic microvessels in the eye. Relying on the mechanism of photoacoustic cavitation, PUT takes advantages of high optical contrast among biological tissues, and can selectively remove microvessels without causing collateral tissue damage.
To achieve personalized treatment with optimal treatment outcome, a multi-modality eye imaging system involving advanced photoacoustic microscopy (PAM) and optical coherence tomography (OCT) has been integrated with the PUT system to provide real-time feedback and online evaluation of the treatment outcome. To assess the performance of this image-guided PUT system, experiments have been conducted on rabbit eye models. During the treatment, cavitation signals were observed and monitored by OCT with good sensitivity, suggesting that OCT can be used to evaluate treatment effect in real time. The PAM was capable of mapping the 3D distributed microvessels with excellent image quality, demonstrating that PAM can help to quantitatively evaluate the treatment outcome. As indicated by the initial results from this study, imaging guidance involving both PAM and OCT could further improve the efficacy and safety of the newly invented PUT, accelerating its translation to ophthalmology clinic.
We propose an automated segmentation method to detect, segment, and quantify hyperreflective foci (HFs) in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT). The algorithm is divided into three stages: preprocessing, layer segmentation, and HF segmentation. In this paper, a supervised classifier (random forest) was used to produce the set of boundary probabilities in which an optimal graph search method was then applied to identify and produce the layer segmentation using the Sobel edge algorithm. An automated grow-cut algorithm was applied to segment the HFs. The proposed algorithm was tested on 20 3-D SD-OCT volumes from 20 patients diagnosed with proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME). The average dice similarity coefficient and correlation coefficient (r) are 62.30%, 96.90% for PDR, and 63.80%, 97.50% for DME, respectively. The proposed algorithm can provide clinicians with accurate quantitative information, such as the size and volume of the HFs. This can assist in clinical diagnosis, treatment, disease monitoring, and progression.
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