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胃肠道息肉标注中的标注工具

Annotation Tools in Gastrointestinal Polyp Annotation.

作者信息

Selnes Ola, Bjørsum-Meyer Thomas, Histace Aymeric, Baatrup Gunnar, Koulaouzidis Anastasios

机构信息

Department of Surgery, Odense University Hospital, 5700 Svendborg, Denmark.

Department of Clinical Research, University of Southern Denmark, 5000 Odense C, Denmark.

出版信息

Diagnostics (Basel). 2022 Sep 26;12(10):2324. doi: 10.3390/diagnostics12102324.

DOI:10.3390/diagnostics12102324
PMID:36292013
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9600922/
Abstract

Capsule endoscopy (CE) is a valid alternative to conventional gastrointestinal (GI) endoscopy tools. In CE, annotation tools are crucial in developing large and annotated medical image databases for training deep neural networks (DNN). We provide an overview of the described and in-use various annotation systems available, focusing on the annotation of adenomatous polyp pathology in the GI tract. Some studies present promising results regarding time efficiency by implementing automated labelling features in annotation systems. Thus, data are inadequate regarding the general overview for users, and may also be more specific on which features provided are necessary for polyp annotation.

摘要

胶囊内镜检查(CE)是传统胃肠道(GI)内镜检查工具的有效替代方法。在CE中,注释工具对于开发用于训练深度神经网络(DNN)的大型带注释医学图像数据库至关重要。我们概述了已描述的和正在使用的各种可用注释系统,重点是胃肠道腺瘤性息肉病理的注释。一些研究通过在注释系统中实现自动标记功能,在时间效率方面取得了有前景的结果。因此,对于用户而言,关于总体概述的数据并不充分,而且对于息肉注释所需提供的哪些特征可能也更具针对性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2d/9600922/7b0be78e1fc4/diagnostics-12-02324-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2d/9600922/f7aed5525eab/diagnostics-12-02324-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2d/9600922/7b0be78e1fc4/diagnostics-12-02324-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2d/9600922/f7aed5525eab/diagnostics-12-02324-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b2d/9600922/7b0be78e1fc4/diagnostics-12-02324-g002.jpg

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A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning.一种基于半弱监督学习的半自动磁共振成像标注算法
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本文引用的文献

1
Fast machine learning annotation in the medical domain: a semi-automated video annotation tool for gastroenterologists.快速机器学习在医学领域的标注:一款用于胃肠病学家的半自动视频标注工具。
Biomed Eng Online. 2022 May 25;21(1):33. doi: 10.1186/s12938-022-01001-x.
2
Capsule endoscopy in gastrointestinal disease: Evaluation, diagnosis, and treatment.胶囊内镜在胃肠道疾病中的应用:评估、诊断和治疗。
Cleve Clin J Med. 2022 Apr 1;89(4):200-211. doi: 10.3949/ccjm.89a.20061.
3
Colon Capsule Endoscopy as a Diagnostic Adjunct in Patients with Symptoms from the Lower Gastrointestinal Tract.
结肠胶囊内镜作为下消化道症状患者的辅助诊断手段
Diagnostics (Basel). 2021 Sep 13;11(9):1671. doi: 10.3390/diagnostics11091671.
4
Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects.新型人工智能驱动软件显著缩短了计算机视觉项目中注释所需的时间。
Endosc Int Open. 2021 Apr;9(4):E621-E626. doi: 10.1055/a-1341-0689. Epub 2021 Apr 14.
5
HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.HyperKvasir,一个用于胃肠道内镜的全面多类图像和视频数据集。
Sci Data. 2020 Aug 28;7(1):283. doi: 10.1038/s41597-020-00622-y.
6
Diagnostic accuracy of capsule endoscopy compared with colonoscopy for polyp detection: systematic review and meta-analyses.胶囊内镜与结肠镜检查诊断息肉的准确性比较:系统评价和荟萃分析。
Endoscopy. 2021 Jul;53(7):713-721. doi: 10.1055/a-1249-3938. Epub 2020 Oct 6.
7
Semi-supervised WCE image classification with adaptive aggregated attention.基于自适应聚合注意力的半监督 WCE 图像分类。
Med Image Anal. 2020 Aug;64:101733. doi: 10.1016/j.media.2020.101733. Epub 2020 Jun 11.
8
Capsule endoscopy for small-intestinal disorders: Current status.胶囊内镜在小肠疾病中的应用:现状。
Dig Endosc. 2019 Sep;31(5):498-507. doi: 10.1111/den.13346. Epub 2019 Feb 10.
9
GTCreator: a flexible annotation tool for image-based datasets.GTCreator:一个用于基于图像数据集的灵活标注工具。
Int J Comput Assist Radiol Surg. 2019 Feb;14(2):191-201. doi: 10.1007/s11548-018-1864-x. Epub 2018 Sep 25.
10
Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images.深度学习与电影渲染:使用逼真的医学图像微调深度神经网络。
Phys Med Biol. 2018 Sep 13;63(18):185012. doi: 10.1088/1361-6560/aada93.