A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides

Autor:	Yongwon Cho, Areum Lee, Heounjeong Go, Hyunna Lee, Beomhee Park, Young-Gon Kim, Namkug Kim, Gyuheon Choi
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	0301 basic medicine Graft Rejection Male Computer science lcsh:Medicine Convolutional neural network Peritubular capillaries Article Giga 03 medical and health sciences 0302 clinical medicine medicine Image Processing Computer-Assisted Humans Medical diagnosis lcsh:Science Multidisciplinary Pixel business.industry lcsh:R Pattern recognition Allografts Immunohistochemistry Kidney Transplantation 030104 developmental biology medicine.anatomical_structure Fully automated Renal allograft Female lcsh:Q Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery
Zdroj:	Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019) Scientific Reports
ISSN:	2045-2322
Popis:	Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of renal allograft rejection. A fully automated system using a convolutional neural network (CNN) was developed to identify regions of interest (ROIs) and to detect C4d positive and negative peritubular capillaries (PTCs) in giga-pixel immunostained slides. The performance of faster R-CNN was evaluated using optimal parameters of the novel method to enlarge the size of labeled masks. Fifty and forty pixels of the enlarged size images showed the best performance in detecting C4d positive and negative PTCs, respectively. Additionally, the feasibility of deep-learning-assisted labeling as independent dataset to enhance detection in this model was evaluated. Based on these two CNN methods, a fully automated system for renal allograft rejection was developed. This system was highly reliable, efficient, and effective, making it applicable to real clinical workflow.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b223b3604b6730425a97315b5fba4c31 http://link.springer.com/article/10.1038/s41598-019-41479-5 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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