Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks

Juan S. Sierra, Jesus Pineda, Eduardo Viteri, Daniela Rueda, Beatriz Tibaduiza, Rúben D. Berrospi, Alejandro Tello, Virgilio Galvis, Giovanni Volpe, Mariá S. Millán, Lenny A. Romero, Andrés G. Marrugo

Resultado de la investigación: Libro / Capitulo del libro / InformeLibros de Investigaciónrevisión exhaustiva

4 Citas (Scopus)

Resumen

Automated cell counting in in-vivo specular microscopy images is challenging, especially in situations where single-cell segmentation methods fail due to pathological conditions. This work aims to obtain reliable cell segmentation from specular microscopy images of both healthy and pathological corneas. We cast the problem of cell segmentation as a supervised multi-class segmentation problem. The goal is to learn a mapping relation between an input specular microscopy image and its labeled counterpart, indicating healthy (cells) and pathological regions (e.g., guttae). We trained a U-net model by extracting 96×96 pixel patches from corneal endothelial cell images and the corresponding manual segmentation by a physician. Encouraging results show that the proposed method can deliver reliable feature segmentation enabling more accurate cell density estimations for assessing the state of the cornea.

Idioma originalInglés
Título de la publicación alojadaApplications of Machine Learning 2020
EditoresMichael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, Khan M. Iftekharuddin
EditorialSPIE
ISBN (versión digital)9781510638280
DOI
EstadoPublicada - 2020
EventoApplications of Machine Learning 2020 - Virtual, Online, Estados Unidos
Duración: 24 ago. 20204 sept. 2020

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
Volumen11511
ISSN (versión impresa)0277-786X
ISSN (versión digital)1996-756X

Conferencia

ConferenciaApplications of Machine Learning 2020
País/TerritorioEstados Unidos
CiudadVirtual, Online
Período24/08/204/09/20

Huella

Profundice en los temas de investigación de 'Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks'. En conjunto forman una huella única.

Citar esto