3D Choroid Neovascularization Growth Prediction with Combined Hyperelastic Biomechanical Model and Reaction-Diffusion Model

Zuo, Chang; Shi, Fei; Zhu, Weifang; Chen, Haoyu; Chen, Xinjian

doi:10.1007/978-3-319-67561-9_16

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“…For example, Banerjee et al 17 and long-term (within 21 months) in the future. Zuo Chang et al 18 combined the reaction-diffusion model with the hyperelastic biomechanical model to predict the growth morphology and volume changes of CNV after anti-VEGF treatment. However, their model only focuses on the independent changes of individual lesion areas, and cannot learn the CNV growth pattern in the whole study population.…”

Section: Introductionmentioning

confidence: 99%

“…Combined with the longitudinal OCT imaging radiology characteristics, demographics, and visual factors, the model can predict the probability of exudation in the eyes of patients in the short‐term (within 3 months) and long‐term (within 21 months) in the future. Zuo Chang et al 18 . combined the reaction‐diffusion model with the hyperelastic biomechanical model to predict the growth morphology and volume changes of CNV after anti‐VEGF treatment.…”

Section: Introductionmentioning

confidence: 99%

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Morphological prognosis prediction of choroid neovascularization from longitudinal SD‐OCT images

et al. 2023

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BackgroundChoroid neovascularization (CNV) has no obvious symptoms in the early stage, but with its gradual expansion, leakage, rupture, and bleeding, it can cause vision loss and central scotoma. In some severe cases, it will lead to permanent visual impairment.PurposeAccurate prediction of disease progression can greatly help ophthalmologists to formulate appropriate treatment plans and prevent further deterioration of the disease. Therefore, we aim to predict the growth trend of CNV to help the attending physician judge the effectiveness of treatment.MethodsIn this paper, we develop a CNN‐based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD‐OCT) B‐scans of each subject at different time points to eliminate retinal displacements of longitudinal data. Then, considering the correlation of longitudinal data, we propose a co‐segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow‐up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder‐recurrent‐decoder framework is developed for CNV growth prediction, in which an attention‐based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations.ResultsThe registration network rigidly registers the follow‐up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state‐of‐the‐art segmentation methods, the proposed segmentation network achieves high performance with the Dice similarity coefficients (Dsc) of 85.27%. Based on the above experiments, the proposed growth prediction network can play a role in predicting the future CNV morphology, and the predicted CNV has a Dsc of 83.69% with the ground truth, which is significantly consistent with the actual follow‐up visit.ConclusionThe proposed registration and segmentation networks provide the possibility for growth prediction. In addition, accurately predicting the growth of CNV enables us to know the efficacy of the drug against individuals in advance, creating opportunities for formulating appropriate treatment plans.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%