Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner
Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner
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DOI：
10.1002/jbio.201700003
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发表时间：
2018-03-01
影响因子：
2.8
通讯作者：
Subrahmanyam, Gorthi R. K. Sai
中科院分区：
文献类型：
--
作者：
Gopakumar, Gopalakrishna Pillai;Swetha, Murali;Subrahmanyam, Gorthi R. K. Sai
本文介绍了一种基于焦点叠加的血液涂片恶性疟原虫自动定量检测方法。对于检测，使用自定义的卷积神经网络（CNN）对图像的焦点堆栈进行操作。将细胞计数问题作为分割问题加以解决，提出了一种两级分割策略。将CNN操作在焦点堆栈上进行疟疾检测是首次，不仅提高了检测精度（灵敏度[97.06%]和特异性[98.50%]），而且有利于对细胞斑块的处理，避免了手工设计特征的需要。切片图像是通过定制的便携式切片扫描仪获得的，该扫描仪由低成本、现成的组件制成，适用于即时诊断。提出的采用复杂算法处理和廉价仪器的方法可能有利于临床医生进行疟疾诊断。
The present paper introduces a focus stacking-based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a 2-level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity [97.06%] and specificity [98.50%]) but also favored the processing on cell patches and avoided the need for hand-engineered features. The slide images are acquired with a custom-built portable slide scanner made from low-cost, off-the-shelf components and is suitable for point-of-care diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis.