Listar por autor "Iarussi, Emmanuel"

Mostrando ítems 1-5 de 5

    • Bone-GAN: Generation of virtual bone microstructure of high resolution peripheral quantitative computed tomography 

      Iarussi, Emmanuel; Thomsen, Felix S. L.; Borggrefe, Jan; Boyd, Steven K.; Wang, Yue; Battié, Michele C. (Medical PhysicsAmerican Association of Physicists in Medicine, 2023)
      Background:Data-driven development of medical biomarkers of bone requires a large amount of image data but physical measurements are generally too restricted in size and quality to perform a robust training. Purpose: ...

    • DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling 

      Fainstein, Miguel; Siless, Viviana; Iarussi, Emmanuel (Universidad Torcuato Di Tella, 2024)
      In recent years, there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. However, UDFs are ...

    • Learning normal asymmetry representations for homologous brain structures 

      Bendersky, Mariana; Iarussi, Emmanuel; Deangeli, Duilio; Princich, Juan Pablo; Larrabide, Ignacio; Orlando, José Ignacio (Universidad Torcuato Di Tella, 2023)
      Although normal homologous brain structures are approximately symmetrical by definition, they also have shape differences due to e.g. natural ageing. On the other hand, neurodegenerative conditions induce their own changes ...

    • NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features 

      Iarussi, Emmanuel; Deangeli, Duilio; Külsgaard, Hernán; Iarussi, Francisco; Braggio, Delfina; Princich, Juan Pablo; Bendersky, Mariana; Larrabide, Ignacio; Orlando, José Ignacio (Brain TopographySpringer, 2023)
      Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer’s Disease. However, current clinical tools rely on either ...

    • VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis 

      Feldman, Paula; Fainstein, Miguel; Siless, Viviana; Iarussi, Emmanuel; Delrieux, Claudio (Universidad Torcuato Di Tella, 2023)
      We present a data-driven generative framework for synthesizing blood vessel 3D geometry. This is a challenging task due to the complexity of vascular systems, which are highly variating in shape, size, and structure. ...