Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library
Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library
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DOI：
10.1136/amiajnl-2012-001380
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发表时间：
2013-11-01
影响因子：
6.4
通讯作者：
Xu, Rong
中科院分区：
文献类型：
--
作者：
Chen, Yang;Ren, Xiaofeng;Xu, Rong
背景视觉信息是医学知识的一个重要方面。建立一个全面的医学影像库，在统一的医学语言系统（UMLS）的精神，将大大有利于病人的教育和自我保健。然而，这样一个大规模的图像库的收集和注释是具有挑战性的。目的联合收割机视觉对象检测技术与医学本体自动挖掘网络照片和检索大量的疾病表现的图像，以最小的手动labelingeffors.Methods作为一个概念的证明，我们首先学习五个器官检测器的三个检测尺度的眼睛，耳朵，嘴唇，手，脚。给定一种疾病，我们使用的信息从UMLS选择受影响的身体部位，运行预训练的器官检测器的网络图像，并结合检测输出检索疾病images.Results相比，需要训练图像的每种疾病的监督图像检索方法，我们的本体引导的方法利用共享的视觉信息的身体部位的疾病。在检索32种疾病的2220幅网络图像时，我们将人工标记工作量减少到15.6%，同时将平均精度从77.7%提高到81.6%，提高了3.9%。对40.6%的疾病，准确率提高了10%。结论网络是一种可行的疾病图像自动检索源，可用于健康图像数据库的建设。我们的方法需要少量的人工努力来收集复杂的疾病图像，并通过标准的医学本体术语来注释它们。
Background Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging.Objective To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort.Methods As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images.Results Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%.Conclusions The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms.