Alessandra Bulanti

Alessandra Bulanti, Alessandro Carfì, Paolo Traverso et al.

With "contact surface area" (CSA) we refers to the area of contact between a tumor and an organ. This indicator has been identified as a predictive factor for surgical peri-operative parameters, particularly in the context of kidney cancer. However, state-of-the-art algorithms for computing the CSA rely on assumptions about the tumor shape and require manual human annotation. In this study, we introduce an innovative method that relies on 3D reconstructions of tumors and organs to provide an accurate and objective estimate of the CSA. Our approach consists of a segmentation protocol for reconstructing organs and tumors from Computed Tomography (CT) images and an algorithm leveraging the reconstructed meshes to compute the CSA. With the aim to contributing to the literature with replicable results, we provide an open-source implementation of our algorithm, along with an easy-to-use graphical user interface to support its adoption and widespread use. We evaluated the accuracy of our method using both a synthetic dataset and reconstructions of 87 real tumor-organ pairs.

Alessandra Bulanti, Alessandro Carfì, Fulvio Mastrogiovanni

Fluent human--robot collaboration requires robots to continuously estimate human behaviour and anticipate future intentions. This entails reasoning jointly about \emph{continuous movements} and \emph{discrete actions}, which are still largely modelled in isolation. In this paper, we introduce \textsf{MA-HERP}, a hierarchical and recursive probabilistic framework for the \emph{joint estimation and prediction} of human movements and actions. The model combines: (i) a hierarchical representation in which movements compose into actions through admissible Allen interval relations, (ii) a unified probabilistic factorisation coupling continuous dynamics, discrete labels, and durations, and (iii) a recursive inference scheme inspired by Bayesian filtering, alternating top-down action prediction with bottom-up sensory evidence. We present a preliminary experimental evaluation based on neural models trained on musculoskeletal simulations of reaching movements, showing accurate motion prediction, robust action inference under noise, and computational performance compatible with on-line human--robot collaboration.