Abstract：
Central Serous Chorioretinopathy (CSCR) is a significant cause of vision impairment worldwide, with Photodynamic Therapy (PDT) emerging as a promising treatment strategy. The capability to precisely segment fluid regions in Optical Coherence Tomography (OCT) scans and predict the response to PDT treatment can substantially augment patient outcomes. This paper introduces a novel deep learning (DL) methodology for automated 3D segmentation of fluid regions in OCT scans, followed by a subsequent PDT response analysis for CSCR patients. Our approach utilizes the rich 3D contextual information from OCT scans to train a model that accurately delineates fluid regions. This model not only substantially reduces the time and effort required for segmentation but also offers a standardized technique, fostering further large-scale research studies. Additionally, by incorporating pre- and post-treatment OCT scans, our model is capable of predicting PDT response, hence enabling the formulation of personalized treatment strategies and optimized patient management. To validate our approach, we employed a robust dataset comprising 2,769 OCT scans (124 3D volumes), and the results obtained were significantly satisfactory, outperforming the current state-of-the-art methods. This research signifies an important milestone in the integration of DL advancements with practical clinical applications, propelling us a step closer towards improved management of CSCR. Furthermore, the methodologies and systems developed can be adapted and extrapolated to tackle similar challenges in the diagnosis and treatment of other retinal pathologies, favoring more comprehensive and personalized patient care.
摘要：
中心性浆液性脉络膜视网膜病变（CSCR）是全球范围内视力障碍的重要原因，光动力疗法（PDT）正在成为一种有前途的治疗策略。在光学相干断层扫描(OCT)扫描中精确分割流体区域并预测对PDT治疗的响应的能力可以显著增强患者结果。本文介绍了一种新颖的深度学习（DL）方法，用于OCT扫描中流体区域的自动3D分割。随后对CSCR患者进行PDT应答分析。我们的方法利用来自OCT扫描的丰富3D上下文信息来训练准确描绘流体区域的模型。该模型不仅大大减少了分割所需的时间和精力，而且提供了一种标准化的技术，促进进一步的大规模研究。此外,通过合并治疗前和治疗后的OCT扫描，我们的模型能够预测PDT响应，因此能够制定个性化治疗策略和优化患者管理。为了验证我们的方法，我们采用了一个强大的数据集，包括2,769个OCT扫描(124个3D体积)，获得的结果非常令人满意，优于当前最先进的方法。这项研究标志着DL进步与实际临床应用整合的重要里程碑，推动我们朝着改善CSCR管理迈出了一步。此外，所开发的方法和系统可以进行调整和推断，以应对其他视网膜病变的诊断和治疗中的类似挑战，有利于更全面和个性化的病人护理。
