Faculty of Mathematics and Computer Science, University of Münster, Einsteinstraße 62, 48149 Münster, Germany.
Institute of Neuro- and Behavioral Biology, University of Münster, Badestraße 9, 48149 Münster, Germany.
Comput Biol Med. 2018 Feb 1;93:189-199. doi: 10.1016/j.compbiomed.2017.12.017. Epub 2017 Dec 29.
The importance of studying model organisms such as Drosophila melanogaster has significantly increased in recent biological research. Amongst others, Drosophila can be used to study heart development and heartbeat related diseases. Here we propose a method for automatic in vivo heartbeat detection of Drosophila melanogaster pupae based on morphological structures which are recorded without any dissection using FIM imaging. Our approach is easy-to-use, has low computational costs, and enables high-throughput experiments. After automatically segmenting the heart region of the pupa in an image sequence, the heartbeat is indirectly determined based on intensity variation analysis. We have evaluated our method using 47,631 manually annotated frames from 29 image sequences recorded with different temporal and spatial resolutions which are made publicly available. We show that our algorithm is both precise since it detects more than 95% of the heartbeats correctly as well as robust since the same standardized set of parameters can be used for all sequences. The combination of FIM imaging and our algorithm enables a reliable heartbeat detection of multiple Drosophila pupae while simultaneously avoiding any time consuming preparation of the animals.
近年来，研究模式生物（如黑腹果蝇）在生物学研究中的重要性显著增加。果蝇可用于研究心脏发育和与心跳相关的疾病。在这里，我们提出了一种基于形态结构的黑腹果蝇蛹活体心跳自动检测方法，该方法使用 FIM 成像进行记录，无需进行任何解剖。我们的方法易于使用，计算成本低，并且能够进行高通量实验。在自动对图像序列中的蛹的心脏区域进行分割后，基于强度变化分析间接确定心跳。我们使用 29 个具有不同时间和空间分辨率的图像序列记录的 47631 个手动注释帧评估了我们的方法，这些图像序列均公开可用。我们表明，我们的算法既精确（因为它正确检测到超过 95％的心跳），又稳健（因为可以为所有序列使用相同标准化的参数集）。FIM 成像和我们的算法的结合可以可靠地检测多个果蝇蛹的心跳，同时避免对动物进行任何耗时的准备。
