Mario Ceresa

TL;DR: A new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducibleThrombus region of interest detection and subsequent fine thrombus segmentation and a new segmentation network architecture, based on Fully convolutional Networks and a Holistically‐Nested Edge Detection Network, is presented.

TL;DR: This review covers state‐of‐the‐art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time.

TL;DR: This article proposes to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection by integrating predictive models of nodulemalignancy into a limited size lung cancer datasets.

TL;DR: Micro-CT-derived descriptors are more sensitive than the other methods compared, to detect in vivo early signs of the disease.

TL;DR: Histomorphometry is the most sensitive technique since it detects airspace enlargement before the other methods (1 h after treatment) and micro-CT correlates well with histology (r2 = 0.63) proving appropriate for longitudinal studies.