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

Lucas Kirsten, Angelo Angonezi, Jose Marques, Fernanda Oliveira, Juliano Faccioni, Camila Cassel, Débora de Sousa, Samlai Vedovatto, Guido Lenz, Claudio Jung; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3996-4005

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.

Related Material

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[bibtex]
@InProceedings{Kirsten_2025_WACV, author = {Kirsten, Lucas and Angonezi, Angelo and Marques, Jose and Oliveira, Fernanda and Faccioni, Juliano and Cassel, Camila and de Sousa, D\'ebora and Vedovatto, Samlai and Lenz, Guido and Jung, Claudio}, title = {Oriented Cell Dataset: A Dataset and Benchmark for Oriented Cell Detection and Applications}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {3996-4005} }