Publications by Federico Bolelli

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/.

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.

Reliable estimates of cellular composition and anatomical distribution in the human brain are essential for biologically plausible circuit models. In the hippocampus, existing reconstructions rely on low-resolution (LR) data without explicit cell-type-resolved annotations, limiting quantitative maps of excitatory neurons, inhibitory interneurons, and glial cells. Using newly released 1 um/px BigBrain sections of the right hippocampus, we present CALHippo, Cellular Annotation Library for the Hippocampus, a multiscale resource for cell-type-resolved reconstruction of the human CA complex. CALHippo includes the first expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1-CA4) subfields with explicit three-class labels, together with a lower-resolution mesoscale cellular point-cloud map. High-resolution (HR) cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, and are classified as excitatory neurons, inhibitory interneurons, or glial cells. To extend sparse HR annotations to the full volume, we project them into the 20 um/px LR BigBrain space and use the resulting class-specific supervision maps to train a UNet-based density estimation model. The predicted density maps enable slice-by-slice inference across the full CA complex and are sampled to generate a class-resolved mesoscale cellular point cloud. Code (https://github.com/AImageLab-zip/CALHippo-Framework) and dataset (https://ditto.ing.unimore.it/calhippo) are publicly released to support reproducibility.

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)

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.

Multimodal Large Language Models (MLLMs) have achieved impressive performance across a variety of vision–language tasks. However, their internal working mechanisms remain largely underexplored. In this work, we introduce FG-TRACER, a framework designed to analyze the information flow between visual and textual modalities in MLLMs in free-form generation. Notably, our numerically stabilized computational method enables the first systematic analysis of multimodal information flow in underexplored domains such as image captioning and chain-of-thought (CoT) reasoning. We apply FG-TRACER to three state-of-the-art MLLMs—LLaVA 1.5, LLaMA 3.2-Vision, and Qwen 2.5-VL—across three vision–language benchmarks—TextVQA, COCO 2014, and ChartQA—and we conduct a word-level analysis of multimodal integration. Our findings uncover distinct patterns of multimodal fusion across models and tasks, demonstrating that fusion dynamics are both model- and task-dependent. Overall, FG-TRACER offers a robust methodology for probing the internal mechanisms of MLLMs in free-form settings, providing new insights into their multimodal reasoning strategies. Our source code is publicly available at https://github.com/AImageLab-zip/FG-TRACER

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.

Brain tumor segmentation is a crucial task in medical imaging that involves the integrated modeling of four distinct imaging modalities to identify tumor regions accurately. Unfortunately, in real-life scenarios, the full availability of such four modalities is often violated due to scanning cost, time, and patient condition. Consequently, several deep learning models have been developed to address the challenge of brain tumor segmentation under conditions of missing imaging modalities. However, the majority of these models have been evaluated using the 2018 version of the BraTS dataset, which comprises only $285$ volumes. In this study, we reproduce and extensively analyze the most relevant models using BraTS2023, which includes 1,250 volumes, thereby providing a more comprehensive and reliable comparison of their performance. Furthermore, we propose and evaluate the adoption of Mamba as an alternative fusion mechanism for brain tumor segmentation in the presence of missing modalities. Experimental results demonstrate that transformer-based architectures achieve leading performance on BraTS2023, outperforming purely convolutional models that were instead superior in BraTS2018. Meanwhile, the proposed Mamba-based architecture exhibits promising performance in comparison to state-of-the-art models, competing and even outperforming transformers. The source code of the proposed approach is publicly released alongside the benchmark developed for the evaluation: https://github.com/AImageLab-zip/IM-Fuse.

Accurate registration of Intraoral Scans (IOS) with Cone-Beam Computed Tomography (CBCT) enables integration of dental crown surfaces with the tooth roots and the surrounding alveolar bone. However, IOS-CBCT registration remains challenging because of limited anatomical overlap, cross-modal differences, and unreliable correspondences. Task 2 of the MICCAI STS 2026 Challenge formulates this setting as a semi-supervised rigid registration problem, in which IOS meshes must be aligned with their corresponding CBCT volumes. For this task, we propose a landmark-driven coarse-to-fine framework in which modality-specific networks predict corresponding dental landmarks from the IOS mesh and CBCT volume. A confidence-weighted RANSAC-Kabsch procedure estimates a robust initial transformation, later refined using point-to-plane Iterative Closest Point. On the challenge validation set, the method achieved a Mean Translation Error of 4.988 mm and a Mean Rotation Error of 1.421°, ranking first on the registration leaderboard at the time of evaluation. The code is available on GitHub: https://github.com/AImageLab-zip/L2L-Registration

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.