{"title":"利用基于多尺度注意力的 U-Net 架构精确分割 OCT 中的视网膜下液体","authors":"Prakash Kumar Karn, Waleed H Abdulla","doi":"10.3390/bioengineering11101032","DOIUrl":null,"url":null,"abstract":"<p><p>This paper presents a deep-learning architecture for segmenting retinal fluids in patients with Diabetic Macular Oedema (DME) and Age-related Macular Degeneration (AMD). Accurate segmentation of multiple fluid types is critical for diagnosis and treatment planning, but existing techniques often struggle with precision. We propose an encoder-decoder network inspired by U-Net, processing enhanced OCT images and their edge maps. The encoder incorporates Residual and Inception modules with an autoencoder-based multiscale attention mechanism to extract detailed features. Our method shows superior performance across several datasets. On the RETOUCH dataset, the network achieved F1 Scores of 0.82 for intraretinal fluid (IRF), 0.93 for subretinal fluid (SRF), and 0.94 for pigment epithelial detachment (PED). The model also performed well on the OPTIMA and DUKE datasets, demonstrating high precision, recall, and F1 Scores. This architecture significantly enhances segmentation accuracy and edge precision, offering a valuable tool for diagnosing and managing retinal diseases. Its integration of dual-input processing, multiscale attention, and advanced encoder modules highlights its potential to improve clinical outcomes and advance retinal disease treatment.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11504175/pdf/","citationCount":"0","resultStr":"{\"title\":\"Precision Segmentation of Subretinal Fluids in OCT Using Multiscale Attention-Based U-Net Architecture.\",\"authors\":\"Prakash Kumar Karn, Waleed H Abdulla\",\"doi\":\"10.3390/bioengineering11101032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper presents a deep-learning architecture for segmenting retinal fluids in patients with Diabetic Macular Oedema (DME) and Age-related Macular Degeneration (AMD). Accurate segmentation of multiple fluid types is critical for diagnosis and treatment planning, but existing techniques often struggle with precision. We propose an encoder-decoder network inspired by U-Net, processing enhanced OCT images and their edge maps. The encoder incorporates Residual and Inception modules with an autoencoder-based multiscale attention mechanism to extract detailed features. Our method shows superior performance across several datasets. On the RETOUCH dataset, the network achieved F1 Scores of 0.82 for intraretinal fluid (IRF), 0.93 for subretinal fluid (SRF), and 0.94 for pigment epithelial detachment (PED). The model also performed well on the OPTIMA and DUKE datasets, demonstrating high precision, recall, and F1 Scores. This architecture significantly enhances segmentation accuracy and edge precision, offering a valuable tool for diagnosing and managing retinal diseases. Its integration of dual-input processing, multiscale attention, and advanced encoder modules highlights its potential to improve clinical outcomes and advance retinal disease treatment.</p>\",\"PeriodicalId\":8874,\"journal\":{\"name\":\"Bioengineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11504175/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioengineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/bioengineering11101032\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioengineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/bioengineering11101032","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
摘要
本文介绍了一种用于分割糖尿病黄斑水肿(DME)和老年性黄斑变性(AMD)患者视网膜液体的深度学习架构。准确分割多种类型的液体对于诊断和治疗计划至关重要,但现有技术往往在精确性方面存在困难。我们提出了一种受 U-Net 启发的编码器-解码器网络,用于处理增强型 OCT 图像及其边缘图。编码器结合了残差模块和插入模块,并采用基于自动编码器的多尺度关注机制来提取细节特征。我们的方法在多个数据集上表现出卓越的性能。在 RETOUCH 数据集上,该网络的视网膜内积液 (IRF) F1 分数达到 0.82,视网膜下积液 (SRF) F1 分数达到 0.93,色素上皮脱落 (PED) F1 分数达到 0.94。该模型在 OPTIMA 和 DUKE 数据集上也表现出色,显示出较高的精确度、召回率和 F1 分数。该架构大大提高了分割准确度和边缘精确度,为诊断和管理视网膜疾病提供了宝贵的工具。它集成了双输入处理、多尺度关注和高级编码器模块,突出了其改善临床结果和推进视网膜疾病治疗的潜力。
Precision Segmentation of Subretinal Fluids in OCT Using Multiscale Attention-Based U-Net Architecture.
This paper presents a deep-learning architecture for segmenting retinal fluids in patients with Diabetic Macular Oedema (DME) and Age-related Macular Degeneration (AMD). Accurate segmentation of multiple fluid types is critical for diagnosis and treatment planning, but existing techniques often struggle with precision. We propose an encoder-decoder network inspired by U-Net, processing enhanced OCT images and their edge maps. The encoder incorporates Residual and Inception modules with an autoencoder-based multiscale attention mechanism to extract detailed features. Our method shows superior performance across several datasets. On the RETOUCH dataset, the network achieved F1 Scores of 0.82 for intraretinal fluid (IRF), 0.93 for subretinal fluid (SRF), and 0.94 for pigment epithelial detachment (PED). The model also performed well on the OPTIMA and DUKE datasets, demonstrating high precision, recall, and F1 Scores. This architecture significantly enhances segmentation accuracy and edge precision, offering a valuable tool for diagnosing and managing retinal diseases. Its integration of dual-input processing, multiscale attention, and advanced encoder modules highlights its potential to improve clinical outcomes and advance retinal disease treatment.
期刊介绍:
Aims
Bioengineering (ISSN 2306-5354) provides an advanced forum for the science and technology of bioengineering. It publishes original research papers, comprehensive reviews, communications and case reports. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. All aspects of bioengineering are welcomed from theoretical concepts to education and applications. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, four key features of this Journal:
● We are introducing a new concept in scientific and technical publications “The Translational Case Report in Bioengineering”. It is a descriptive explanatory analysis of a transformative or translational event. Understanding that the goal of bioengineering scholarship is to advance towards a transformative or clinical solution to an identified transformative/clinical need, the translational case report is used to explore causation in order to find underlying principles that may guide other similar transformative/translational undertakings.
● Manuscripts regarding research proposals and research ideas will be particularly welcomed.
● Electronic files and software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material.
● We also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds.
Scope
● Bionics and biological cybernetics: implantology; bio–abio interfaces
● Bioelectronics: wearable electronics; implantable electronics; “more than Moore” electronics; bioelectronics devices
● Bioprocess and biosystems engineering and applications: bioprocess design; biocatalysis; bioseparation and bioreactors; bioinformatics; bioenergy; etc.
● Biomolecular, cellular and tissue engineering and applications: tissue engineering; chromosome engineering; embryo engineering; cellular, molecular and synthetic biology; metabolic engineering; bio-nanotechnology; micro/nano technologies; genetic engineering; transgenic technology
● Biomedical engineering and applications: biomechatronics; biomedical electronics; biomechanics; biomaterials; biomimetics; biomedical diagnostics; biomedical therapy; biomedical devices; sensors and circuits; biomedical imaging and medical information systems; implants and regenerative medicine; neurotechnology; clinical engineering; rehabilitation engineering
● Biochemical engineering and applications: metabolic pathway engineering; modeling and simulation
● Translational bioengineering