Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation

no code implementations • 6 Feb 2025 • Tewele W. Tareke, Neree Payan, Alexandre Cochet, Laurent Arnould, Benoit Presles, Jean-Marc Vrigneaud, Fabrice Meriaudeau, Alain Lalande

After fine-tuning, the model demonstrated a DSC of 0. 78 and a HD of 4. 95 mm on PET_Fu exams.

Active Learning Image Segmentation +2

Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model

no code implementations • MIDL 2019 • Abdul Qayyum, Alain Lalande, Thomas Decourselle, Thibaut Pommier, Alexandre Cochet, Fabrice Meriaudeau

The proposed model could be used for the automatic segmentation of myocardial border that is a very important step for accurate quantification of no-reflow, myocardial infarction, myocarditis, and hypertrophic cardiomyopathy, among others.

LV Segmentation Segmentation

Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks

no code implementations • 9 Jan 2019 • Ezequiel de la Rosa, Désiré Sidibé, Thomas Decourselle, Thibault Leclercq, Alexandre Cochet, Alain Lalande

Although the technique accurately reflects the damaged tissue, there is no clinical standard for quantifying myocardial infarction (MI), demanding most algorithms to be expert dependent.

Segmentation