Oriented Cell Dataset: efficient imagery analyses using angular representation
In this work, we propose a new public dataset for cell detection in bright-field microscopy images annotated with Oriented Bounding Boxes (OBBs), named Oriented Cell Dataset (OCD). We show that OBBs provide a more accurate shape representation compared to standard Horizontal Bounding Boxes (HBBs), with slight overhead of one extra click in the annotation process. Our dataset also contains a subset of images with five independent expert annotations, which allows inter-annotation analysis to determine if the results produced by algorithms are within the expected variability of human experts. We investigated how to automate cell biology microscopy images by training seven popular OBB detectors in the proposed dataset, and focused our analyses on two main problems in cancer biology: cell confluence and polarity determination, the latter not possible through HBB representation. All models achieved statistically similar results to the biological applications compared to human annotation, enabling the automation of cell biology and cancer cell biology microscopy image analysis. Our code and dataset are available at https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB.