Abstract
Multiple Sclerosis (MS) is a chronic, often disabling, autoimmune disease affecting the central nervous system and characterized by demyelination and neuropathic alterations. Magnetic Resonance (MR) images plays a pivotal role in the diagnosis and the screening of MS. MR images identify and localize demyelinating lesions (or plaques) and possible associated atrophic lesions whose MR aspect is in relation with the evolution of the disease. We propose a novel MS lesions segmentation method for MR images, based on Convolutional Neural Networks (CNNs) and partial self-supervision and studied the pros and cons of using self-supervision for the current segmentation task. Investigating the transferability by freezing the firsts convolutional layers, we discovered that improvements are obtained when the CNN is retrained from the first layers. We believe such results suggest that MRI segmentation is a singular task needing high level analysis from the very first stages of the vision process, as opposed to vision tasks aimed at day-to-day life such as face recognition or traffic sign classification. The evaluation of segmentation quality has been performed on full image size binary maps assembled from predictions on image patches from an unseen database.
Highlights
Multiple sclerosis (MS) is a central nervous system autoimmune disease
We adapted it to take five 3D patches as input, respectively extracted from the T1 weighted (T1W), T1 weighted with contrast enhancing agent (T1Wc), T2 weighted (T2W), T2weighted-fluid-attenuated inversion recovery (FLAIR), and proton density weighted (PDW) images as such sequences are generally analyzed by the radiologist to evaluate the presence of multiple sclerosis lesions
Our technique improves the quality of MS lesion segmentation, not yet as much as expected, and not that way it should
Summary
Multiple sclerosis (MS) is a central nervous system autoimmune disease. It affects 1 to more than 200 in 100 000 people depending on the region [1], it generally appears near 30 years old [2] and can rapidly induce high disability [3]. Annotated patient datasets have been made publicly available too, making it easier to explore the capacity of machine learning algorithms such as CNNs to synthesize semantics from medical images. Last researches in this field study the importance of some parameters and suggest different techniques to improve segmentation, most of them use CNNs. Nair et al proposed in [7] to resort the Montecarlo dropout in CNN to access to segmentation indicators such as prediction variability. McKinley et al [11] demonstrated that simultaneous segmenting WM lesions and brain tissues improves the quality of segmentation and Brosch et al [12] pre-trained their CNN with convolutional restricted Boltzmann machines in an unsupervised way to improve segmentation performances
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