Publications by Lorenzo Borghi

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.

Accurate delineation of maxillofacial anatomy in Cone-Beam Computed Tomography (CBCT) is essential for dental planning, but robust automated segmentation remains challenging, due to limited public multi-structure datasets and the high computational burden of 3D deep learning models. We present and release ToothFairy3, a large-scale CBCT benchmark that extends ToothFairy2 with 102 additional fully annotated scans and an expanded taxonomy covering 77 classes, including 32 tooth-specific pulp cavities and small neurovascular structures. ToothFairy3 comprises 582 volumes (over 40000 annotated objects), with 532 released with voxel-level labels and 50 held out for leakage-free, server-side evaluation. We also introduce U-Mamba2, an efficient U-Net-style architecture that inserts a Mamba2 state-space block at the bottleneck to capture global context with favorable computational scaling. Our proposed domain-informed training further improves the learning of maxillofacial anatomies. Across CNN, Transformer, and Mamba baselines, U-Mamba2 achieves competitive Dice/HD95 scores with lower latency and, compared with training on state-of-the-art public CBCT datasets, ToothFairy3-trained models generalize best to the hidden test set, particularly for maxillary structures.

We introduce Bits2Bites, the first publicly available dataset for occlusal classification from intra-oral scans, comprising 200 paired upper and lower dental arches annotated across multiple clinically relevant dimensions (sagittal, vertical, transverse, and midline relationships). Leveraging this resource, we propose a multi-task learning benchmark that jointly predicts five occlusal traits from raw 3D point clouds using state-of-the-art point-based neural architectures. Our approach includes extensive ablation studies assessing the benefits of multi-task learning against single-task baselines, as well as the impact of automatically-predicted anatomical landmarks as input features. Results demonstrate the feasibility of directly inferring comprehensive occlusion information from unstructured 3D data, achieving promising performance across all tasks. Our entire dataset, code, and pretrained models are publicly released to foster further research in automated orthodontic diagnosis.