Moura, Joaquim deNovo Buján, JorgeOrtega Hortas, MarcosBarreira, NoeliaCharlón, Pablo2024-06-072024-06-072021J. de Moura, J. Novo, M. Ortega, N. Barreira and M. G. Penedo, "Automated Segmentation of the Central Serous Chorioretinopathy fluid regions using Optical Coherence Tomography Scans," 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Aveiro, Portugal, 2021, pp. 1-6, doi: 10.1109/CBMS52027.2021.00008.978-166544121-61063-7125http://hdl.handle.net/2183/368362021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Aveiro, Portugal, 2021This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/CBMS52027.2021.00008[Abstract]: Central serous chorioretinopathy is one of the most frequent causes of vision impairment among middle-aged adults. Optical Coherence Tomography (OCT) is a non-invasive diagnostic technique that is commonly used for the monitoring of this relevant eye disease. In this context, this paper proposes a fully automatic system for the characterization of intraretinal pathological fluid regions associated with central serous chori-oretinopathy using OCT scans. To achieve this, we adapted an end-to-end fully convolutional architecture for semantic pixel-wise segmentation. The proposed methodology was tested using a heterogeneous set of 100 OCT scans of different patients. Satisfactory results were obtained, reaching values of 0.9954pm 0.0007, 0.8792 \pm 0.0079 and 0.9651 \pm 0.0041 for the mean Accuracy, mean Jaccard index and mean Dice coefficient, respectively. The proposed system also demonstrated its competitive performance with respect to other state-of-the-art approaches.eng© 2021 IEEECentral serous chorioretinopathyComputer-aided diagnosisOptical coherence tomographyRetinal imagingSegmentationconference outputopen access10.1109/CBMS52027.2021.00008