Automation of Wilms' tumor segmentation by artificial intelligence.

Background: 3D reconstruction of Wilms' tumor provides several advantages but are not systematically performed because manual segmentation is extremely time-consuming. The objective of our study was to develop an artificial intelligence tool to automate the segmentation of tumors and kidneys in children.

Methods: A manual segmentation was carried out by two experts on 14 CT scans.

A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals.

The intracranial pressure (ICP) signal, as monitored on patients in intensive care units, contains pulses of cardiac origin, where P1 and P2 subpeaks can often be observed. When calculable, the ratio of their relative amplitudes is an indicator of the patient's cerebral compliance. This characterization is particularly informative for the overall state of the cerebrospinal system.

Risk prediction in surgery using case-based reasoning and agent-based modelization.

Managing the risks arising from the actions and conditions of the various elements that make up an operating room is a major concern during a surgical procedure. One of the main challenges is to define alert thresholds in a non-deterministic context where unpredictable adverse events occur. In response to this problematic, this paper presents an architecture that couples a Multi-Agent System (MAS) with Case-Based Reasoning (CBR).

3D reconstruction of Wilms' tumor and kidneys in children: Variability, usefulness and constraints.

Introduction: Wilms' tumor (WT) is the most common type of malignant kidney tumor in children. Three-dimensional reconstructions can be performed pre-operatively to help surgeons in the planning phase.

Objectives: The main objective of this study was to determine the variability of WT segmentation and 3D reconstruction.

Fusion of multiple segmentations of medical images using OVASSION and Deep Learning methods: Application to CT-Scans for tumoral kidney.

Nephroblastoma is the most common kidney tumour in children. Its diagnosis is based on imagery. In the SAIAD project, we have designed a platform for optimizing the segmentation of deformed kidney and tumour with a small dataset, using Artificial Intelligence methods.