Publications by Gabriele Rosati

Cone-beam computed tomography (CBCT) is widely used for dento-maxillofacial diagnostics and treatment planning, and comprehensive multi-structure segmentation remains time-consuming, limiting large-scale, reproducible research. In this article, we present ToothFairy2, a MICCAI 2024 challenge on multi-structure segmentation in maxillofacial CBCT. The accompanying dataset comprises 530 CBCT volumes (480 public training, 50 hidden test) with expert 3D annotations of 42 classes, including maxilla, mandible, crowns, bridges, implants, inferior alveolar canals, maxillary sinuses, pharynx, and teeth using the International Tooth Numbering System (FDI). 26 international teams participated in ToothFairy2, and their methods were run and evaluated for voxel-wise multi-class segmentation using a standardized protocol. This report extends the evaluation of teeth to also investigate the current capabilities of tooth detection and FDI numbering. Furthermore, ranking stability was analyzed to assess the robustness of the final challenge outcome. Overall, challenge participants achieved consistently high performance for large, high-contrast structures such as jawbones, pharynx, and most teeth, while maxillary sinuses, dental restorations, and fine structures remain challenging due to class imbalance and metal artifacts. Analysis of tooth-related metrics further revealed that assigning correct FDI numbers was more challenging than delineating individual teeth. By releasing CBCT data, 3D annotations, baseline models, and evaluation code, ToothFairy2 establishes a long-term benchmark to drive the development of automated methods for robust, clinically meaningful multi-structure segmentation in maxillofacial CBCT.

The presence of visible particles in pharmaceutical products is a critical quality issue that demands strict monitoring. Recently, Convolutional Neural Networks (CNNs) have been widely used in industrial settings to detect defects, but there remains a gap in the literature concerning the detection of particles floating in liquid substances, mainly due to the lack of publicly available datasets. In this study, we focus on the detection of foreign particles in pharmaceutical liquid vials, leveraging two state-of-the-art deep-learning approaches adapted to our specific multiclass problem. The first methodology employs a standard ResNet-18 architecture, while the second exploits a Multi-Instance Learning (MIL) technique to efficiently deal with multiple images (sequences) of the same sample. To address the issue of no data availability, we devised and partially released an annotated dataset consisting of sequences containing 19 images for each sample, captured from rotating vials, both with and without impurities. The dataset comprises 2,426 sequences for a total of 46,094 images labeled at the sequence level and including five distinct classes. The proposed methodologies, trained on this new extensive dataset, represent advancements in the field, offering promising strategies to improve the safety and quality control of pharmaceutical products and setting a benchmark for future comparisons.