The diameter of central serous chorioretinopathy (CSCR) lesion is one of the important indicators to evaluate the severity of CSCR and the efficacy of corresponding treatment schemes. Traditional manual measurement by ophthalmologists is usually based on a single or a small number of optical coherence tomography (OCT) B-scan images. This measurement scheme may not be convincing, vulnerable to subjective factors and lower efficiency. To alleviate the above situation, this paper proposes an intelligent key boundary point location method for all B-scan images of a single patient to assist in the rapid and accurate diameter measurement of the CSCR lesion area. Firstly, an initial location module (ILM) based on the multi-task learning paradigm is appropriately adjusted and introduced into the key boundary point location task, which preliminarily realizes the rapid location of key boundary points. Secondly, to further ameliorate the ILM, a gradient based correction module (GBCM) is designed, followed by the construction of the cascade model (ILM-GBCM) which improves the location accuracy of key boundary points as a whole. Extensive experiments based on five different convolutional neural network (CNN) backbones are carried out, revealing the feasibility of ILM in this task and the effectiveness of ILM-GBCM. On 912 testing images, the maximum correction ratio reaches 83.66%, and the minimum location time at the image level is as low as 0.1754 s, which not only confirms the necessity of correction operation, but also greatly reduce the time cost of ophthalmologists' manual measurement operation in clinic.
Keywords: Cascade model; Central serous chorioretinopathy; Correction module; Initial location module; Multi-task learning.
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