Although antibodies are well developed and widely used in cancer therapy and diagnostic fields, some defects remain, such as poor tissue penetration, long in vivo metabolic retention, potential cytotoxicity, patent...
Molecular imaging plays important roles in many fields, including disease diagnosis, therapeutic efficacy evaluation, intraoperative imaging guidance, drug metabolism monitoring, and patient selection for appropriate treatment. As a key component, the targeting ligand determines the specificity, affinity, and in vivo performance of molecular imaging probes. In this review, high‐throughput screening and biological display platforms for the discovery of ligands applicable to molecular imaging are briefly reviewed. Basic information on ligand development for molecular imaging is first introduced, followed by a presentation of various selection platforms and typical or iterative cases. The features, advantages, limitations, and application scope of screening and display platforms are compared and discussed. Last, a basic selection strategy and a perspective for protein‐based ligands are provided.
Background
Fundus microvasculature may be visually observed by ophthalmoscope and has been widely used in clinical practice. Due to the limitations of available equipment and technology, most studies only utilized the two-dimensional planar features of the fundus microvasculature.
Methods
This study proposed a novel method for establishing the three-dimensional fundus vascular structure model and generating hemodynamic characteristics based on a single image. Firstly, the fundus vascular are segmented through our proposed network framework. Then, the length and width of vascular segments and the relationship among the adjacent segments are collected to construct the three-dimensional vascular structure model. Finally, the hemodynamic model is generated based on the vascular structure model, and highly correlated hemodynamic features are selected to diagnose the ophthalmic diseases.
Results
In fundus vascular segmentation, the proposed network framework obtained 98.63% and 97.52% on Area Under Curve (AUC) and accuracy respectively. In diagnosis, the high correlation features extracted based on the proposed method achieved 95% on accuracy.
Conclusions
This study demonstrated that hemodynamic features filtered by relevance were essential for diagnosing retinal diseases. Additionally, the method proposed also outperformed the existing models on the levels of retina vessel segmentation. In conclusion, the proposed method may represent a novel way to diagnose retinal related diseases, which can analysis two-dimensional fundus pictures by extracting heterogeneous three-dimensional features.
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