Abstract
Accurate segmentation of retinal images plays a crucial role in aiding ophthalmologists in diagnosing retinopathy of prematurity (ROP) and assessing its severity. However, due to their underdeveloped, thinner vessels, manual annotation in infant fundus images is very complex, and this presents challenges for fully supervised learning. To address the scarcity of annotations, we propose a semi supervised segmentation framework designed to advance ROP studies without the need for extensive manual vessel annotation. Unlike previous methods that rely solely on limited labeled data, our approach leverages teacher student learning by integrating two powerful components: an uncertainty weighted vessel unveiling module and domain adversarial learning. The vessel unveiling module helps the model effectively reveal obscured and hard to detect vessel structures, while adversarial training aligns feature representations across different domains, ensuring robust and generalizable vessel segmentations. We validate our approach on public datasets (CHASEDB, STARE) and an in-house ROP dataset, demonstrating its superior performance across multiple evaluation metrics. Additionally, we extend the model's utility to a downstream task of ROP multi-stage classification, where vessel masks extracted by our segmentation model improve diagnostic accuracy. The promising results in classification underscore the model's potential for clinical application, particularly in early-stage ROP diagnosis and intervention. Overall, our work offers a scalable solution for leveraging unlabeled data in pediatric ophthalmology, opening new avenues for biomarker discovery and clinical research.
Abstract (translated)
视网膜图像的精确分割在帮助眼科医生诊断早产儿视网膜病变（ROP）及评估其严重程度方面起着至关重要的作用。然而，由于婴儿视网膜血管发育不完全且较薄，人工标注婴儿眼底图像非常复杂，这给全监督学习带来了挑战。为了解决标注数据稀缺的问题，我们提出了一种半监督分割框架，旨在推动ROP研究而无需大量的人工血管标注。与之前仅依赖有限标签数据的方法不同，我们的方法通过结合两个强大的组件——基于不确定性的血管揭示模块和领域对抗性学习来实现师生学习。该血管揭示模块帮助模型有效揭示被遮挡且难以检测的血管结构，而对抗训练则使特征表示在不同域之间保持一致，确保了健壮且具有泛化能力的血管分割。我们在公共数据集（CHASEDB, STARE）和内部ROP数据集上验证了我们的方法，在多个评估指标中展示了其优越性能。此外，我们将模型的应用扩展到了ROP多阶段分类这一下游任务，其中由我们的分割模型提取出的血管掩码提高了诊断准确性。分类中的积极结果突显了该模型在临床应用中的潜力，特别是在早期ROP诊断和干预方面。总体而言，我们的工作提供了一种利用未标注数据可扩展解决方案，在儿科眼科领域开启了生物标志物发现和临床研究的新途径。
URL
https://arxiv.org/abs/2411.09140
