Luis Kabongo

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 article presents and discusses the implementation of a direct volume rendering system for the Web, which articulates a large portion of the rendering task in the client machine, using the capabilities of the recently released WebGL standard.

TL;DR: A novel architecture specifically designed to ease the SEEG trajectory planning using the 3D Slicer platform as a basis is presented and improved manual planned trajectories in 98% of cases in terms of quantitative indexes, even when applying more conservative criteria with respect to actual clinical practice.

TL;DR: A real-time volume rendering component for the Web, which provides a set of illustrative and non-photorealistic styles to several volume data types, offering a suitable tool for declarative volume rendering on the Web.

TL;DR: This work proposes a methodology that analyses retrospective planning data and builds a set of average trajectories, representing the practice of a clinical centre, which can be mapped to a new patient to initialise planning procedure.