Exploration of machine learning methods for brain tumor segmentation

Description : Conférencier : Seyed Mohammad Havaei, étudiant au doctorat à l’Université de Sherbrooke

Résumé : Malignant brain tumors are the second leading cause of cancer related deaths in children under 20 1 . There are nearly 700,000 people in the U.S. living with a brain tumor and 17,000 people are likely to loose their lives due to primary malignant and central nervous system brain tumor every year. To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI scan of the brain is acquired followed by a manual examination of the scan by an expert who looks for lesions (i.e. cluster of cells which deviate from healthy tissue). For treatment purposes, the tumor and its sub-regions are outlined in a procedure known as brain tumor segmentation . Although brain tumor segmentation is primarily done manually, it is very time consuming and the segmentation is subject to variations both between observers and within the same observer. To address these issues a number of automatic and semi-automatic methods have been proposed over the years to help physicians in the decision making process.

Methods based on machine learning have been subject of great interest in brain tumor segmentation. With the advent of deep learning methods and their success in many computer vision applications such as image classification, these methods have also started to gain popularity in medical image analysis.

In this thesis, we explore different machine learning and deep learning methods applied to brain tumor segmentation.

Membre du jury, président rapporteur : Shengrui Wang, professeur, Département d’informatique, Faculté des sciences, Université de Sherbrooke
Membre du jury, directeur de recherche : Hugo Larochelle, professeur, Département d’informatique, Faculté des sciences, Université de Sherbrooke
Membre du jury, codirecteur de recherche : Pierre-Marc Jodoin, professeur, Département d’informatique, Faculté des sciences, Université de Sherbrooke
Membre du jury, évaluateur interne à l’Université de Sherbrooke : Jean-Pierre Dussault, professeur, Département d’informatique
Membre du jury, évaluateur externe à l’Université de Sherbrooke : Langis Gagnon, directeur R-D et directeur scientifique, Centre de Recherche Informatique de Montréal

Toutes les personnes intéressées sont cordialement invitées.