Randomness-Restricted Diffusion Model for Ocular Surface Structure Segmentation

Xinyu Guo;Han Wen;Huaying Hao;Yifan Zhao;Yanda Meng;Jiang Liu;Yalin Zheng;Wei Chen;Yitian Zhao
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Abstract

Ocular surface diseases affect a significant portion of the population worldwide. Accurate segmentation and quantification of different ocular surface structures are crucial for the understanding of these diseases and clinical decision-making. However, the automated segmentation of the ocular surface structure is relatively unexplored and faces several challenges. Ocular surface structure boundaries are often inconspicuous and obscured by glare from reflections. In addition, the segmentation of different ocular structures always requires training of multiple individual models. Thus, developing a one-model-fits-all segmentation approach is desirable. In this paper, we introduce a randomness-restricted diffusion model for multiple ocular surface structure segmentation. First, a time-controlled fusion-attention module (TFM) is proposed to dynamically adjust the information flow within the diffusion model, based on the temporal relationships between the network’s input and time. TFM enables the network to effectively utilize image features to constrain the randomness of the generation process. We further propose a low-frequency consistency filter and a new loss to alleviate model uncertainty and error accumulation caused by the multi-step denoising process. Extensive experiments have shown that our approach can segment seven different ocular surface structures. Our method performs better than both dedicated ocular surface segmentation methods and general medical image segmentation methods. We further validated the proposed method over two clinical datasets, and the results demonstrated that it is beneficial to clinical applications, such as the meibomian gland dysfunction grading and aqueous deficient dry eye diagnosis.
用于眼表结构分段的随机性受限扩散模型
眼表疾病影响着全世界很大一部分人口。准确分割和量化不同的眼表结构对了解这些疾病和临床决策至关重要。然而,眼表结构的自动分割是一个相对未开发的领域,面临着一些挑战。眼表结构边界通常不明显,被反射的眩光所遮蔽。此外,不同眼结构的分割往往需要训练多个单独的模型。因此,开发一种适合所有模型的分割方法是可取的。本文提出了一种基于随机限制扩散的多眼表面结构分割模型。首先,基于网络输入与时间的时间关系,提出了一个时间控制的融合注意模块(TFM)来动态调整扩散模型内的信息流。TFM使网络能够有效地利用图像特征来约束生成过程的随机性。我们进一步提出了一种低频一致性滤波器和一种新的损失来减轻多步去噪过程中模型的不确定性和误差积累。大量的实验表明,我们的方法可以分割七种不同的眼表结构。该方法优于专用眼表分割方法和一般医学图像分割方法。我们在两个临床数据集上进一步验证了所提出的方法,结果表明该方法有利于临床应用,如睑板腺功能障碍分级和水缺乏性干眼诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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