Multiple Sclerosis Lesion Synthesis in MRI Using an Encoder-Decoder U-NET
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In this paper, we propose generating synthetic multiple sclerosis (MS) lesions on MRI images with the final aim to
improve the performance of supervised machine learning algorithms, therefore avoiding the problem of the lack of
available ground truth. We propose a two-input two-output fully convolutional neural network model for MS lesion
synthesis in MRI images. The lesion information is encoded as discrete binary intensity level masks passed to the
model and stacked with the input images. The model is trained end-to-end without the need for manually annotating
the lesions in the training set. We then perform the generation of synthetic lesions on healthy images via registration
of patient images, which are subsequently used for data augmentation to increase the performance for supervised MS
lesion detection algorithms. Our pipeline is evaluated on MS patient data from an in-house clinical dataset and the
public ISBI2015 challenge dataset. The evaluation is based on measuring the similarities between the real and the
synthetic images as well as in terms of lesion detection performance by segmenting both the original and synthetic
images individually using a state-of-the-art segmentation framework. We also demonstrate the usage of synthetic MS
lesions generated on healthy images as data augmentation. We analyze a scenario of limited training data (one-image
training) to demonstrate the effect of the data augmentation on both datasets. Our results significantly show the
effectiveness of the usage of synthetic MS lesion images. For the ISBI2015 challenge, our one-image model trained
using only a single image plus the synthetic data augmentation strategy showed a performance similar to that of
other CNN
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