Intraoral photographs (IOPs) provide a low-cost record of tooth appearance, alignment, soft tissue, and occlusal relationships. We present IOP-Compass, a dataset, benchmark, and annotation resource built on 1,000 patients from the Bite2Text collection. It comprises 5,000 standardized clinical photographs, five views per patient, with view labels and expert-verified tooth-instance masks carrying FDI numbers, together with frozen patient-disjoint splits. The dataset was produced through a browser-based human-in-the-loop annotation platform that we also released. Using IOP-Compass, we benchmark a pipeline representative of the current literature for view classification, region-of-interest extraction, and FDI-aware tooth instance segmentation, providing reference results and ablations across alternative pipeline components. View classification is near-saturated on the benchmark, while FDI-aware instance segmentation remains the main challenge. Both the dataset and code are publicly released.
A Public Dataset for Tooth Segmentation in Multi-View Intraoral Photographs / Zelelew, Y.A., Borghi, L., Marchesini, K., Lugli, M., Grana, C., Bolelli, F.. - (2026). (Oral and Dental Image Analysis Workshop Strasbourg, France Sep 27-Oct 1).
A Public Dataset for Tooth Segmentation in Multi-View Intraoral Photographs
Yibeltal Assefa Zelelew;Lorenzo Borghi;Kevin Marchesini;Matteo Lugli;Costantino Grana;Federico Bolelli
2026
Abstract
Intraoral photographs (IOPs) provide a low-cost record of tooth appearance, alignment, soft tissue, and occlusal relationships. We present IOP-Compass, a dataset, benchmark, and annotation resource built on 1,000 patients from the Bite2Text collection. It comprises 5,000 standardized clinical photographs, five views per patient, with view labels and expert-verified tooth-instance masks carrying FDI numbers, together with frozen patient-disjoint splits. The dataset was produced through a browser-based human-in-the-loop annotation platform that we also released. Using IOP-Compass, we benchmark a pipeline representative of the current literature for view classification, region-of-interest extraction, and FDI-aware tooth instance segmentation, providing reference results and ablations across alternative pipeline components. View classification is near-saturated on the benchmark, while FDI-aware instance segmentation remains the main challenge. Both the dataset and code are publicly released.| File | Dimensione | Formato | |
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