Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation

• Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp. 3 - 11
• Main Authors: Brosch, Tom, Yoo, Youngjin, Tang, Lisa Y. W., Li, David K. B., Traboulsee, Anthony, Tam, Roger
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2015
• Series: Lecture Notes in Computer Science
• Subjects: convolutional neural nets; deep learning; machine learning; MRI; multiple sclerosis lesions; Segmentation; unbalanced classification
• ISBN: 9783319245737; 3319245732
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-24574-4_1

We propose a novel segmentation approach based on deep convolutional encoder networks and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that has both convolutional and deconvolutional layers, and combines feature extraction and segmentation prediction in a single model. The joint training of the feature extraction and prediction layers allows the model to automatically learn features that are optimized for accuracy for any given combination of image types. In contrast to existing automatic feature learning approaches, which are typically patch-based, our model learns features from entire images, which eliminates patch selection and redundant calculations at the overlap of neighboring patches and thereby speeds up the training. Our network also uses a novel objective function that works well for segmenting underrepresented classes, such as MS lesions. We have evaluated our method on the publicly available labeled cases from the MS lesion segmentation challenge 2008 data set, showing that our method performs comparably to the state-of-theart. In addition, we have evaluated our method on the images of 500 subjects from an MS clinical trial and varied the number of training samples from 5 to 250 to show that the segmentation performance can be greatly improved by having a representative data set.