In this paper we propose the Hatching-Box, a novel imaging and analysis system to automatically monitor and quantify the developmental behavior of Drosophila in standard rearing vials and during regular rearing routines, rendering explicit experiments obsolete. This is achieved by combining custom tailored imaging hardware with dedicated detection and tracking algorithms, enabling the quantification of larvae, filled/empty pupae and flies over multiple days. Given the affordable and reproducible design of the Hatching-Box in combination with our generic client/server-based software, the system can easily be scaled to monitor an arbitrary amount of rearing vials simultaneously. We evaluated our system on a curated image dataset comprising nearly 470,000 annotated objects and performed several studies on real world experiments. We successfully reproduced results from well-established circadian experiments by comparing the eclosion periods of wild type flies to the clock mutants $\textit{per}^{short}$, $\textit{per}^{long}$ and $\textit{per}^0$ without involvement of any manual labor. Furthermore we show, that the Hatching-Box is able to extract additional information about group behavior as well as to reconstruct the whole life-cycle of the individual specimens. These results not only demonstrate the applicability of our system for long-term experiments but also indicate its benefits for automated monitoring in the general cultivation process.

翻译：本文提出孵化盒，一种新型成像与分析系统，用于在标准饲养管和常规饲养流程中自动监测和量化果蝇的发育行为，从而无需进行显式实验。该系统通过将定制成像硬件与专用检测追踪算法相结合，实现了对幼虫、饱满/空蛹及成蝇在连续多日内的量化分析。得益于孵化盒经济可复制的设计，并结合我们基于客户端/服务器的通用软件，该系统可轻松扩展以同时监测任意数量的饲养管。我们在一个包含近47万个标注对象的精选图像数据集上评估了本系统，并进行了多项真实实验研究。通过比较野生型果蝇与时钟突变体$\textit{per}^{short}$、$\textit{per}^{long}$和$\textit{per}^0$的羽化周期，我们在无需任何人工干预的情况下成功复现了经典昼夜节律实验的结果。此外，我们证明孵化盒能够提取群体行为的附加信息，并重建个体样本的完整生命周期。这些结果不仅展示了本系统在长期实验中的适用性，也表明了其在通用培育过程中进行自动化监测的优势。