Publications by Nicola Morelli

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/.

Testicular ultrasound imaging is vital for assessing male infertility, with testicular inhomogeneity serving as a key biomarker. However, subjective interpretation and the scarcity of publicly available datasets pose challenges to automated classification. In this study, we explore supervised and unsupervised pretraining strategies using a ResNet-based architecture, supplemented by diffusion-based generative models to synthesize realistic ultrasound images. Our results demonstrate that pretraining significantly enhances classification performance compared to training from scratch, and synthetic data can effectively substitute real images in the pretraining process, alleviating data-sharing constraints. These methods offer promising advancements toward robust, clinically valuable automated analysis of male infertility. The source code is publicly available at https://github.com/AImageLab-zip/TesticulUS/.