Automatic Segmentation and Visualisation of the Epirretinal Membrane in OCT Scans Using Densely Connected Convolutional Networks
| Autor: | Mateo Gende, Pablo Charlón, Marcos Ortega, Joaquim de Moura, Jorge Novo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Jaccard index
genetic structures medicine.diagnostic_test Pixel Optical coherence tomography Computer science business.industry Medical diagnostic imaging Distortion (optics) Deep learning medicine.disease eye diseases Visualization Epiretinal membrane Machine learning medicine Computer vision Segmentation sense organs Artificial intelligence business |
| Zdroj: | RUC. Repositorio da Universidade da Coruña instname |
| Popis: | Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021. [Abstract] The Epiretinal Membrane (ERM) is an ocular disease that appears as a fibro-cellular layer of tissue over the retina, specifically, over the Inner Limiting Membrane (ILM). It causes vision blurring and distortion, and its presence can be indicative of other ocular pathologies, such as diabetic macular edema. The ERM diagnosis is usually performed by visually inspecting Optical Coherence Tomography (OCT) images, a manual process which is tiresome and prone to subjectivity. In this work, we present a methodology for the automatic segmentation and visualisation of the ERM in OCT volumes using deep learning. By employing a Densely Connected Convolutional Network, every pixel in the ILM can be classified into either healthy or pathological. Thus, a segmentation of the region susceptible to ERM appearance can be produced. This methodology also produces an intuitive colour map representation of the ERM presence over a visualisation of the eye fundus created from the OCT volume. In a series of representative experiments conducted to evaluate this methodology, it achieved a Dice score of 0.826±0.112 and a Jaccard index of 0.714±0.155. The results that were obtained demonstrate the competitive performance of the proposed methodology when compared to other works in the state of the art. This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the predoctoral and postdoctoral grant contracts ref. ED481A 2021/161 and ED481B 2021/059, respectively; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%) Xunta de Galicia; ED481A 2021/161 Xunta de Galicia; ED481B 2021/059 Xunta de Galicia; ED431C 2020/24 Xunta de Galicia; IN845D 2020/38 Xunta de Galicia; ED431G 2019/01 |
| Databáze: | OpenAIRE |
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