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In some severe cases, it will lead to permanent visual impairment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Accurate 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this paper, we develop a CNN\u2010based method for CNV growth prediction. To achieve this, we first design a registration network to rigidly register the spectral domain optical coherence tomography (SD\u2010OCT) B\u2010scans 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\u2010segmentation network with a correlation attention guidance (CAG) module to cooperatively segment CNV lesions of a group of follow\u2010up images and use them as input for growth prediction. Finally, based on the above registration and segmentation networks, an encoder\u2010recurrent\u2010decoder framework is developed for CNV growth prediction, in which an attention\u2010based gated recurrent unit (AGRU) is embedded as the recurrent neural network to recurrently learn robust representations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The registration network rigidly registers the follow\u2010up images of patients to the reference images with a root mean square error (RMSE) of 6.754 pixels. And compared with other state\u2010of\u2010the\u2010art 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\u2010up visit.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The 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.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16294","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T06:25:05Z","timestamp":1676442305000},"page":"4839-4853","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Morphological prognosis prediction of choroid neovascularization from longitudinal SD\u2010OCT images"],"prefix":"10.1002","volume":"50","author":[{"given":"Jiayan","family":"Shen","sequence":"first","affiliation":[{"name":"MIPAV Lab, School of Electronics and Information Engineering Soochow University  Suzhou Jiangsu Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyue","family":"Chen","sequence":"additional","affiliation":[{"name":"MIPAV Lab, School of Electronics and Information Engineering Soochow University  Suzhou Jiangsu Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Peng","sequence":"additional","affiliation":[{"name":"MIPAV Lab, School of Electronics and Information Engineering Soochow University  Suzhou Jiangsu Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ocular Fundus Diseases Shanghai General Hospital  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenan","family":"Xu","sequence":"additional","affiliation":[{"name":"MIPAV Lab, School of Electronics and Information Engineering Soochow University  Suzhou Jiangsu Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weifang","family":"Zhu","sequence":"additional","affiliation":[{"name":"MIPAV Lab, School of Electronics and Information Engineering Soochow University  Suzhou Jiangsu Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyun","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ocular Fundus Diseases Shanghai General Hospital  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinjian","family":"Chen","sequence":"additional","affiliation":[{"name":"MIPAV Lab, School of Electronics and Information Engineering Soochow University  Suzhou Jiangsu Province China"},{"name":"State Key Laboratory of Radiation Medicine and Protection Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2015.12.001"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0161-6420(97)30368-6"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-0031-1281583"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/IAE.0b013e31827d260a"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1755-3768.2011.02237.x"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1159\/000355091"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcjo.2014.04.018"},{"key":"e_1_2_8_9_1","doi-asserted-by":"crossref","unstructured":"NiemeijerM GarvinM LeeK GinnekenB Abr\u00e0moffM SonkaM.Registration of 3D spectral OCT volumes using 3D SIFT feature point matching. 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