Publications by Costantino Grana

We present a dataset and baseline for ontology-grounded structured prediction from dental Cone-Beam Computed Tomography (CBCT) volumes. Building on the public ToothFairy3 benchmark (532 volumes with expert-level segmentations), we contribute (i) a total of 893 free-text clinical reports for 529 publicly available CBCT volumes, (ii) their conversion into validated RDF/Turtle (Resource Description Framework) instances aligned with a clinician-designed OWL (Web Ontology Language) ontology spanning 13 finding types and multiple qualifier axes, and (iii) a strong baseline demonstrating the effectiveness of our setup and establishing a foundation for future work. We formulate CBCT reporting as a three-stage structured prediction problem—i.e., finding detection, anatomical slot allocation, and property prediction—and introduce a hierarchical evaluation suite of six clinically interpretable metrics that decouple detection, localization, and characterization. A baseline model using frozen multi-scale VoxTell features, a structure-indexed encoder, and ontology-driven prediction heads achieves strong results under 5-fold cross-validation, with stage-decoupled analysis identifying presence detection as the primary deployment bottleneck. Dataset, ontology, and code are publicly released: https://github.com/AImageLab-zip/CBCT-Report

Atrial fibrillation (AF) is a common cardiac arrhythmia associated with increased risk of stroke and cardiovascular complications. Although electrocardiography (ECG) remains the diagnostic gold standard, remote photoplethysmography (rPPG) has emerged as a promising non-contact approach for extracting pulse-related information from facial videos and enabling unobtrusive rhythm screening. This systematic review investigates the current evidence on facial-video-based rPPG methods for AF detection. Following a PRISMA-based workflow, records were identified from PubMed, Embase, Scopus, IEEE Xplore, and additional search sources. After removing 151 duplicates, 358 records were screened by title and abstract. Of these, 36 full-text reports were assessed for eligibility. Two additional studies, not retrieved by the database search, were identified through reference-list screening, resulting in 19 studies being included in the review. The reviewed literature shows that recent computer-vision and deep learning approaches have improved robustness to motion artifacts, illumination changes, and acquisition noise, often achieving promising detection performance in controlled settings. However, the field remains limited by the small number of available studies, heterogeneous datasets, and scarce clinical validation. Moreover, within the reviewed literature, widespread regulatory-cleared clinical deployment of camera-based rPPG systems for remote AF detection was not identified, indicating that further methodological standardization, larger clinical studies, and regulatory-oriented validation are needed before this technology can become a reliable tool for real-world AF screening.

Visual Autoregressive (VAR) models face a severe "Memory Wall" on edge devices due to large model size and substantial KV-cache requirements. In this work, we analyze the Infinity VAR family (2B and 8B) and propose a compression pipeline for deployment on constrained NVIDIA Jetson systems. We diagnose critical bottlenecks: activation outliers reaching 353x the median and channel-skewed cache variance. To address this, we propose a hybrid pipeline combining SVDQuant—to structurally decouple weight outliers—and Asymmetric Per-Channel KV8 quantization. Our approach reduces the Infinity-8B footprint by 64% (37.1GB →13.3GB), fitting it on the mid-range Orin NX with a 4.1x speedup over Flux.1-dev (W4A4), while achieving superior aesthetic alignment (ImageReward 1.13 vs 0.935). Crucially, we also unlock entry-level feasibility for the Infinity-2B, compressing it from 16.0 to 7.71 GB to enable deployment on the Orin Nano. These results establish a new efficiency standard for high-fidelity generative AI at the edge. The code is available at https://github.com/Henvezz95/deepcompressor.

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.

Reproducibility policies promise "checkable" medical-imaging science, yet many submissions still ship unverifiable artifacts. Our analysis of 3722 MICCAI papers shows code-linking rising from 51.8% (2021) to 72.5% (2025), but ~13% of linked repositories are inaccessible or empty. We present paper-snitch, a reviewer-facing decision-support tool that turns these signals into an evidence-grounded report. Paper-snitch parses PDFs, resolves and sanity-checks repositories, and applies policy-aware checklists aligned with MICCAI expectations, producing a review-time verifiability score decomposed into interpretable sub-scores plus criterion-linked excerpts and artifacts reviewers can inspect. It never executes untrusted code or attempts GPU-heavy reproduction, focusing instead on bounded, verifiable checks. We compare paper-snitch on 100 randomly sampled MICCAI 2025 papers with human annotators using shared evaluation criteria, indicating that automated, bounded checks can scale reproducibility screening while keeping final decisions with reviewers.

Male infertility is a significant yet under-addressed global health condition, and testicular ultrasound (US) plays a central role in its diagnostic evaluation. We introduce and publicly release TesticulUS-Real, the first multicenter testicular US segmentation dataset, addressing the absence of annotated public benchmarks for this anatomy. The dataset comprises 1,053 real ultrasound images acquired from two independent clinical institutions, with expert segmentation masks obtained through a standardized annotation and consensus review protocol. Leveraging this resource, we define an open-organ segmentation protocol to evaluate how models trained on existing multi-organ US data transfer to a previously unseen anatomical target. Beyond the dataset release, we conduct a broad cross-organ segmentation generalization study on ultrasound data. Using the UUSIC benchmark, we evaluate generalization across five anatomical regions and independent acquisition centers, comparing task-specific segmentation models, generalization-oriented ultrasound methods, and SAM-based foundation models under fully automatic inference. Alongside this benchmark, we introduce Cond-UNet, a lightweight conditional U-Net that combines Feature-wise Linear Modulation (FiLM) with our newly proposed shared attention conditioning (SAC) to obtain adaptive organ-aware representations. Experiments show that Cond-UNet achieves the best average cross-organ generalization performance across the UUSIC organs while using fewer parameters and lower computational cost than foundation-model alternatives. In the open-organ setting, the proposed testicular dataset enables a direct analysis of how different model families behave when facing an unseen ultrasound anatomy, highlighting the role of large-scale pretraining for foundation models and the robustness of organ-aware conditioning in lightweight architectures. The code is publicly released at https://github.com/AImageLab-zip/US_Cond-UNet, and the dataset at https://ditto.ing.unimore.it/testiculus/.

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.

Progress in dental computer vision is limited by the absence of large-scale multimodal datasets that jointly capture 3D intraoral geometry and 2D appearance across diverse clinical settings. Existing resources are typically unimodal, which hinders robust cross-modal learning and generalization. We assemble and release a multi-center dataset of 1,000 patients comprising 2,000 registered upper/lower intraoral scans, 5,000 paired intraoral photographs, and 2,403 clinician-authored reports. This combination links detailed 3D dental geometry with complementary 2D evidence, supporting occlusal and orthodontic analysis. Moreover, to enable scalable and privacy-preserving acquisition and annotation across distributed centers, we introduce an open platform that supports multimodal ingestion and structured labeling. Experiments indicate that state-of-the-art multimodal models fail to generate clinically faithful reports, motivating geometry-aware adaptation. We therefore propose IOS-Qwen, which fuses a PointTransformer 3D encoder with Qwen3-VL to generate structured, point-cloud-conditioned reports. Together, the dataset, the platform, and the baselines establish a foundation for multimodal dental AI research. Code is publicly released (https://github.com/AImageLab-zip/IOS-Report)

High-resolution histological imaging provides essential detail for quantitative brain modeling, yet acquiring whole-brain data at micrometer scale remains technically and economically challenging. This work introduces Brain-SR, a diffusion-based super-resolution framework designed to reconstruct high-resolution cortical sections from low-resolution BigBrain data. Building upon the InvSR paradigm, our method performs resolution enhancement in the latent space of a pretrained variational autoencoder, guided by a task-specific noise-predictor network. A key contribution is a frequency-domain supervision term that compares the magnitude spectra of predicted and target patches, enforcing spectral consistency while remaining robust to local misalignments. Quantitative evaluations demonstrate that Brain-SR achieves substantial improvements in LPIPS (-27%) and FID (-58%) compared to baseline diffusion Super-Resolution, while spectral analysis confirms accurate recovery of the frequency distribution. The resulting reconstructions preserve neuronal structures consistent with high-resolution references, offering a practical step toward large-scale, morphologically faithful brain histology reconstruction. The code is publicly available to support reproducibility: https://github.com/AImageLab-zip/Brain-SR.

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.