eprintid: 19086
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/90/86
datestamp: 2024-06-04 14:11:32
lastmod: 2024-06-04 14:11:32
status_changed: 2024-06-04 14:04:50
type: conference_item
metadata_visibility: show
creators_name: Wang, Y.K.
creators_name: Kai Wen, K.
creators_name: Lu, C.-K.
creators_name: Lin, C.-H.
title: Retinal Layer and Fluid Segmentation with Transformer Based Architecture
ispublished: pub
keywords: Brain mapping; Computer aided diagnosis; Convolutional neural networks; Deep learning; Image segmentation; Learning algorithms; Multilayer neural networks; Network architecture; Ophthalmology; Optical tomography, Convolutional neural network; Critical tasks; Deep learning; Fluid segmentation; High-accuracy; Manual segmentation; Retinal disease; Retinal image; Retinal layers; Vision transformer, Convolution
note: cited By 0; Conference of 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 ; Conference Date: 17 July 2023 Through 19 July 2023; Conference Code:192266
abstract: Retinal layer and fluid segmentation is a critical task in assisting doctors to diagnose retinal diseases. Manual segmentation by experts provides the highest accuracy, but it is time-consuming and inconsistent if segmented by different experts. Deep learning algorithms(e.g. Convolutional Neural Network(CNN)) have provided a faster way to perform segmentation through a computer-aided diagnosis system. Nevertheless, CNN has limitations, such as a limited receptive field and loss of details. In this project, we propose a transformer-based architecture to segment the retinal layer and fluid from retinal images. The architecture is based on Vision Transformer (ViT) and modified to improve performance. The transformer has been trained on a set of training retinal images and evaluated on a separate set of testing retinal images. The transformer-based architecture demonstrated a 0.01 improvement in average dice coefficient compared to the Unet architecture for fluid and layer segmentation. The Transformer-based architecture is better suited for deployment in commercial portable Optical Coherence Tomography (OCT) devices due to significantly faster inference speed. The proposed model is at most 4 times higher than that of the CNN family models. This makes it an ideal choice for resource-constrained environments where computational resources are limited. © 2023 IEEE.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174903554&doi=10.1109%2fICCE-Taiwan58799.2023.10226946&partnerID=40&md5=d29ca45159bc5c695c523d0eaf6b05b0
id_number: 10.1109/ICCE-Taiwan58799.2023.10226946
full_text_status: none
publication: 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
pagerange: 409-410
refereed: TRUE
citation:   Wang, Y.K. and Kai Wen, K. and Lu, C.-K. and Lin, C.-H.  (2023) Retinal Layer and Fluid Segmentation with Transformer Based Architecture.  In: UNSPECIFIED.