Optical Coherence Tomography (OCT) facilitates a comprehensive examination of macular edema and associated lesions. Manual delineation of retinal fluid is labor-intensive and error-prone, necessitating an automated diagnostic and therapeutic planning mechanism. Conventional supervised learning models are hindered by dataset limitations, while Transformer-based large vision models exhibit challenges in medical image segmentation, particularly in detecting small, subtle lesions in OCT images. This paper introduces the Multidimensional Directionality-Enhanced Retinal Fluid Segmentation framework (MD-DERFS), which reduces the limitations inherent in conventional supervised models by adapting a transformer-based large vision model for macular edema segmentation. The proposed MD-DERFS introduces a Multi-Dimensional Feature Re-Encoder Unit (MFU) to augment the model’s proficiency in recognizing specific textures and pathological features through directional prior extraction and an Edema Texture Mapping Unit (ETMU), a Cross-scale Directional Insight Network (CDIN) furnishes a holistic perspective spanning local to global details, mitigating the large vision model’s deficiencies in capturing localized feature information. Additionally, the framework is augmented by a Harmonic Minutiae Segmentation Equilibrium loss (LHMSE) that can address the challenges of data imbalance and annotation scarcity in macular edema datasets. Empirical validation on the MacuScan-8k dataset shows that MD-DERFS surpasses existing segmentation methodologies, demonstrating its efficacy in adapting large vision models for boundary-sensitive medical imaging tasks. The code is publicly available at https://github.com/IMOP-lab/MD-DERFS-Pytorch.git.

中文翻译：

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通过大型视觉模型进行多维方向性增强分割


光学相干断层扫描 （OCT） 有助于全面检查黄斑水肿和相关病变。视网膜液的手动描绘是劳动密集型且容易出错的，因此需要自动诊断和治疗计划机制。传统的监督学习模型受到数据集限制的阻碍，而基于 Transformer 的大型视觉模型在医学图像分割方面表现出挑战，尤其是在检测 OCT 图像中细微的病变方面。本文介绍了多维方向性增强视网膜液体分割框架 （MD-DERFS），该框架通过调整基于 transformer 的大视觉模型进行黄斑水肿分割，减少了传统监督模型固有的局限性。拟议的 MD-DERFS 引入了一个多维特征再编码器单元 （MFU），以增强模型通过定向先验提取识别特定纹理和病理特征的能力，以及一个水肿纹理映射单元 （ETMU），一个跨尺度定向洞察网络 （CDIN） 提供了一个跨越局部到全局细节的整体视角，减轻了大型视觉模型在捕获局部特征信息方面的缺陷。此外，该框架还通过谐波细节分割平衡损失 （LHMSE） 得到增强，可以解决黄斑水肿数据集中数据不平衡和注释稀缺的挑战。MacuScan-8k 数据集的实证验证表明，MD-DERFS 超越了现有的分割方法，证明了它在使大型视觉模型适应边界敏感的医学成像任务方面的功效。该代码在 https://github.com/IMOP-lab/MD-DERFS-Pytorch.git 上公开提供。