Biomedical Engineering department seminar

Speaker: Ariel Birenbaum,

School of Electrical Engineering

Multi-View Longitudinal CNN for Multiple Sclerosis Lesion Segmentation

Abstract

This work presents a deep-learning based automated method for Multiple Sclerosis (MS) lesion segmentation. Automatic segmentation of MS lesions is a challenging task due to their variability in shape, size, location and texture in brain Magnetic Resonance (MR) images.

In the proposed scheme, MR intensities and White Matter priors are used to extract candidate lesion voxels, following which Convolutional Neural Networks are utilized for false positive reduction and to obtain the final segmentation result.

The proposed networks use longitudinal data, a novel contribution in the domain of MS lesion analysis.

The method obtained state-of-the-art results on the 2015 Longitudinal MS Lesion Segmentation Challenge dataset, and achieved a performance level equivalent to a trained human rater. Automatic segmentation methods, such as the one proposed, once proven in accuracy and robustness, can help diagnosis and patient follow-up while reducing the time consuming need of manual segmentation.

העבודה נעשתה בהנחיית פרופ' חיית גרינשפן מהמחלקה להנדסה ביו-רפואית,

 אוניברסיטת תל-אביב

ההרצאה תתקיים ביום ראשון 19.03.17, בשעה 14:30

בחדר 315, הבניין הרב תחומי, אוניברסיטת תל אביב