Segmentation of retinal fluids and hyperreflective foci using deep learning approach in optical coherence tomography scans

Yeison D. Sanchez, Bernardo Quijano, Fabio D. Padilla, Oscar Perdomo, Fabio A. González

Resultado de la investigación: Capítulo en Libro/Reporte/ConferenciaCapítulo (revisado por pares)revisión exhaustiva

Resumen

Retinal diseases are a common cause of blindness around the world, early detection of clinical findings can help to avoid vision loss in patients. Optical coherence tomography images have been widely used to diagnose retinal diseases, due to the capacity to show in detail findings as drusen, hyperreflective foci, and intraretinal and subretinal fluids. The location of findings is vital to identify and follow-up the retinal disease. However, the detection and segmentation of these findings is not an easy task due to artifacts noise, and the time consuming even to experts ophthalmologist. This paper proposes a computational method based on deep learning to automatically identify fluids and hyperreflective foci as a tool to identify retinal diseases through the use of OCT images. The method was evaluated on a set of OCT images manually annotated by experts. The experimental results present a Dice coefficient of 0,4437 and 0,6245 in the segmentation task of fluids (intraretinal fluids and subretinal fluids) and hyperreflective foci, respectively.

Idioma originalInglés estadounidense
Título de la publicación alojada16th International Symposium on Medical Information Processing and Analysis
EditoresEduardo Romero, Natasha Lepore, Jorge Brieva, Marius Linguraru
EditorialSPIE
ISBN (versión digital)9781510639911
DOI
EstadoPublicada - nov 3 2020
Evento16th International Symposium on Medical Information Processing and Analysis 2020 - Lima, Virtual, Perú
Duración: oct 3 2020oct 4 2020

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
Volumen11583
ISSN (versión impresa)0277-786X
ISSN (versión digital)1996-756X

Conferencia

Conferencia16th International Symposium on Medical Information Processing and Analysis 2020
País/TerritorioPerú
CiudadLima, Virtual
Período10/3/2010/4/20

All Science Journal Classification (ASJC) codes

  • Materiales electrónicos, ópticos y magnéticos
  • Física de la materia condensada
  • Informática aplicada
  • Matemáticas aplicadas
  • Ingeniería eléctrica y electrónica

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