Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears.
Crowdsourcing malaria parasite quantification: an online game for analyzing images of infected thick blood smears.
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DOI：
10.2196/jmir.2338
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发表时间：
2012-11-29
影响因子：
7.4
通讯作者：
Frean J
中科院分区：
文献类型：
--
作者：
Luengo-Oroz MA;Arranz A;Frean J
全世界每天有60万新的疟疾病例。估计寄生虫负担和疾病相应严重程度的金标准包括通过显微镜手动计数血涂片中的寄生虫数量，这一过程可能需要专业显微镜工作者20多分钟的时间。这项研究测试了众包方法进行疟疾图像分析的可行性。特别是，我们调查了没有经验的匿名志愿者是否能够通过玩基于网络的游戏在厚血涂片的数字化图像中计数疟疾寄生虫。该实验系统由一个基于网络的游戏组成，其中在线志愿者的任务是检测数字化血液样本图像中的寄生虫，再加上一个决策算法，该算法结合了几个玩家的分析，以产生改进的集体检测结果。通过MalariaSpot网站收集数据。随机图像的厚血膜含有恶性疟原虫在中等至低寄生虫血症，获得了传统的光学显微镜，向球员。在游戏中，玩家必须在1分钟内找到并标记尽可能多的寄生虫。如果玩家发现图像中存在所有寄生虫，他们将获得一个新的图像。为了将不同玩家的选择联合收割机组合成一个单一的群体决策，我们实现了一个图像处理管道和一个仲裁算法，当一组玩家同意其位置时，该算法判断寄生虫标记。在一个多月的时间里，来自95个国家的匿名玩家玩了超过12，000场游戏，并在测试图像上生成了超过270，000次点击的数据库。结果显示，结合22个游戏，从非专家球员实现了寄生虫计数准确率高于99%。这一表现也可以通过组合13场比赛的球员训练1分钟。详尽的计算测量了所有玩家的寄生虫计数准确度，作为所考虑的游戏数量和玩家经验的函数。此外，我们提出了一个数学方程，准确地模拟集体寄生虫计数性能。这项研究验证了在线游戏方法，用于在厚血膜图像中对疟疾寄生虫进行众包计数。研究结果支持了这样的结论，即非专家能够快速学习如何识别数字化厚血液样本中疟疾寄生虫的典型特征，并且结合几个用户的分析提供了与专家显微镜相似的寄生虫计数准确率。该实验说明了众包游戏方法用于执行常规疟疾寄生虫定量的潜力，以及更普遍地用于解决生物医学图像分析问题的潜力，以及未来与全球健康挑战相关的远程诊断的潜力。
There are 600,000 new malaria cases daily worldwide. The gold standard for estimating the parasite burden and the corresponding severity of the disease consists in manually counting the number of parasites in blood smears through a microscope, a process that can take more than 20 minutes of an expert microscopist’s time. This research tests the feasibility of a crowdsourced approach to malaria image analysis. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count malaria parasites in digitized images of thick blood smears by playing a Web-based game. The experimental system consisted of a Web-based game where online volunteers were tasked with detecting parasites in digitized blood sample images coupled with a decision algorithm that combined the analyses from several players to produce an improved collective detection outcome. Data were collected through the MalariaSpot website. Random images of thick blood films containing Plasmodium falciparum at medium to low parasitemias, acquired by conventional optical microscopy, were presented to players. In the game, players had to find and tag as many parasites as possible in 1 minute. In the event that players found all the parasites present in the image, they were presented with a new image. In order to combine the choices of different players into a single crowd decision, we implemented an image processing pipeline and a quorum algorithm that judged a parasite tagged when a group of players agreed on its position. Over 1 month, anonymous players from 95 countries played more than 12,000 games and generated a database of more than 270,000 clicks on the test images. Results revealed that combining 22 games from nonexpert players achieved a parasite counting accuracy higher than 99%. This performance could be obtained also by combining 13 games from players trained for 1 minute. Exhaustive computations measured the parasite counting accuracy for all players as a function of the number of games considered and the experience of the players. In addition, we propose a mathematical equation that accurately models the collective parasite counting performance. This research validates the online gaming approach for crowdsourced counting of malaria parasites in images of thick blood films. The findings support the conclusion that nonexperts are able to rapidly learn how to identify the typical features of malaria parasites in digitized thick blood samples and that combining the analyses of several users provides similar parasite counting accuracy rates as those of expert microscopists. This experiment illustrates the potential of the crowdsourced gaming approach for performing routine malaria parasite quantification, and more generally for solving biomedical image analysis problems, with future potential for telediagnosis related to global health challenges.
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影响因子：
64.8
作者：
通讯作者：
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影响因子：
2.2
作者：
Sio, Selena W. S.;Sun, Weiling;Tan, Kevin S. W.
通讯作者：
Tan, Kevin S. W.
DOI：
10.1073/pnas.1014748109
发表时间：
2012-04-24
影响因子：
11.1
作者：
Barrington, Luke;Turnbull, Douglas;Lanckriet, Gert
通讯作者：
Lanckriet, Gert
使用数学形态自动检测外周血涂片的工作区域。
DOI：
10.1155/2003/642562
发表时间：
2003
期刊：
Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology
影响因子：
--
作者：
Angulo J;Flandrin G
通讯作者：
Flandrin G
影响因子：
4.5
作者：
Tek, F. Boray;Dempster, Andrew G.;Kale, Izzet
通讯作者：
Kale, Izzet