Oriented Cell Dataset: A Dataset and Benchmark for Oriented Cell Detection and Applications

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Lucas N. Kirsten, Angelo Angonezi, Jose Marques, Fernanda Oliveira, Juliano Faccioni, Camila Cassel, Débora Santos de Sousa, Samlai Vedovatto, Guido Lenz, Cláudio R. Jung

Published: 2025, Last Modified: 02 Mar 2026WACV 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: This work presents a new public dataset for cell detection in bright-field microscopy images annotated with Oriented Bounding Boxes (OBBs), named Oriented Cell Dataset (OCD). Our dataset also contains a subset of images with five independent expert annotations, which allows inter-annotation analysis to determine a suitable IoU acceptance threshold for evaluating cell detectors. We show that OBBs and a derived representation, Oriented Ellipses (OEs), provide a more accurate shape representation than standard Horizontal Bounding Boxes (HBBs) with a slight overhead of one extra click in the annotation process. We benchmarked OCD using 14 state-of-the-art oriented object detectors, and explored two main problems in cancer biology: cell confluence and polarity determination. Our code and dataset are available at https://github.com/LucasKirsten/Deep-Cell-Tracking-EBB.