基于最优传输的图匹配用于3D视网膜OCT图像配准
Optimal Transport-based Graph Matching for 3D retinal OCT image registration
AI总结：
本文提出基于最优传输图匹配（OT-GM）的3D小鼠视网膜OCT图像配准框架，结合自适应加权血管图描述符和3D立方体描述符进行x-y平面对齐，并利用ILM和HR分割引导z方向配准，在合理时间内优于现有方法。
摘要：
纵向光学相干断层扫描（OCT）图像的配准有助于疾病监测，并在图像融合应用中至关重要。小鼠视网膜OCT图像常用于葡萄膜炎等眼病模型的纵向研究，但与人类成像相比其质量通常较差。本文提出了一种新颖且高效的框架，包含基于最优传输的图匹配（OT-GM）方法，用于3D小鼠OCT图像配准。我们首先对类眼底图像进行配准，这些图像通过将一个体积的所有b-scan投影到与其正交的平面上获得，以下简称x-y平面。我们引入自适应加权血管图描述符（AWVGD）和3D立方体描述符（CD），以识别从OCT投影图像中分割出的血管所提取图的节点之间的对应关系。AWVGD包含缩放、平移和旋转，计算效率高，而CD利用3D空间和频域信息。OT-GM方法随后在x-y平面执行正确对齐。最后，沿正交于x-y平面方向（z方向）的配准由小鼠b-scan特有的两个重要解剖特征——内界膜（ILM）和玻璃体残留物（HR）的分割引导。主客观评估结果均表明，我们的框架在合理执行时间内优于其他成熟方法于小鼠OCT图像上的表现。
英文摘要：
Registration of longitudinal optical coherence tomography (OCT) images assists disease monitoring and is essential in image fusion applications. Mouse retinal OCT images are often collected for longitudinal study of eye disease models such as uveitis, but their quality is often poor compared with human imaging. This paper presents a novel but efficient framework involving an optimal transport based graph matching (OT-GM) method for 3D mouse OCT image registration. We first perform registration of fundus-like images obtained by projecting all b-scans of a volume on a plane orthogonal to them, hereafter referred to as the x-y plane. We introduce Adaptive Weighted Vessel Graph Descriptors (AWVGD) and 3D Cube Descriptors (CD) to identify the correspondence between nodes of graphs extracted from segmented vessels within the OCT projection images. The AWVGD comprises scaling, translation and rotation, which are computationally efficient, whereas CD exploits 3D spatial and frequency domain information. The OT-GM method subsequently performs the correct alignment in the x-y plane. Finally, registration along the direction orthogonal to the x-y plane (the z-direction) is guided by the segmentation of two important anatomical features peculiar to mouse b-scans, the Internal Limiting Membrane (ILM) and the hyaloid remnant (HR). Both subjective and objective evaluation results demonstrate that our framework outperforms other well-established methods on mouse OCT images within a reasonable execution time.
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