###### Abstract  
Multiple Sclerosis (MS) is a neuroinflammatory demyelinating disease that affects over 2,000,000 individuals worldwide. It is characterized by white matter lesions that are identified through the segmentation of magnetic resonance images (MRIs). Manual segmentation is very time-intensive because radiologists spend a great amount of time labeling T1-weighted, T2-weighted, and FLAIR MRIs. In response, deep learning models have been created to reduce segmentation time by automatically detecting lesions. These models often use individual MRI sequences as well as combinations, such as FLAIR\({}^{2}\), which is the multiplication of FLAIR and T2 sequences. Unlike many other studies, this seeks to determine an optimal MRI sequence, thus reducing even more time by not having to obtain other MRI sequences. With this consideration in mind, four Convolutional Encoder Networks (CENs) with different network architectures (U-Net, U-Net++, Linknet, and Feature Pyramid Network) were used to ensure that the optimal MRI applies to a wide array of deep learning models. Each model had used a pretrained ResNeXt-50 encoder in order to conserve memory and to train faster. Training and testing had been performed using two public datasets with 30 and 15 patients. [PERSON]'s exact test was used to evaluate statistical significance, and the automatic segmentation times were compiled for the top two models. This work determined that FLAIR is the optimal sequence based on Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). By using FLAIR, the U-Net++ with the ResNeXt-50 achieved a high DSC of 0.7159.  
convolutional encoder networks; convolutional neural networks; machine learning; magnetic resonance image; multiple sclerosis; segmentation; white matter lesions +
Footnote †: journal: Applied Sciences  
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Footnote †: journal: Applied Sciences  
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These MRI sequences look distinct even though they can be representations of the same brain.  
A significant issue with current diagnostic procedures is that there is a large amount of ambiguity and no gold standard with respect to an MRI sequence for the segmentation of this disease [4]. Due to this, many radiologists use T1-weighted, T2-weighted, and FLAIR sequences for segmentation. However, current methods of manual segmentation are already fairly time intensive [5]; thus, the time required to obtain multiple sequences adds unnecessary delays in the overall pipeline for segmenting a patient's lesions.  
Therefore, the key contributions of this work come from the process used in determining an optimal MRI sequence. Most studies have not tried to find an optimal sequence for their models [6] and have, thus, not accounted for the time that would come from obtaining the MRIs in a realistic clinical setting. Although there are a few studies that have investigated discrepancies among different MRI sequences, the majority of these studies use a single model in their methodology [7]. This raises a significant question since there is no guarantee that those results would apply to a robust model that might interpret or use features very differently. Hence, the most significant contribution of this work is that it uses different models to ensure that discrepancies in performances are directly attributed to the MRI sequences.  
Additionally, this work also focuses on Convolutional Encoder Networks (CENs), which are CNNs that replace the regular encoding pathway with a preexisting encoder. Encoders are networks that solely downsample the input image and extract its features, usually for the purpose of classification. In this work, the decoding pathways of the U-Net, U-Net++, Linknet, and Feature Pyramid Network (FPN) were combined with the ResNeXt-50 encoder. To the best of the authors' knowledge, this is the first work to use these model combinations specifically for the automatic segmentation of MS.