  Objective: Multiple Sclerosis (MS) is an autoimmune, and demyelinating
disease that leads to lesions in the central nervous system. This disease can
be tracked and diagnosed using Magnetic Resonance Imaging (MRI). Up to now a
multitude of multimodality automatic biomedical approaches is used to segment
lesions which are not beneficial for patients in terms of cost, time, and
usability. The authors of the present paper propose a method employing just one
modality (FLAIR image) to segment MS lesions accurately. Methods: A patch-based
Convolutional Neural Network (CNN) is designed, inspired by 3D-ResNet and
spatial-channel attention module, to segment MS lesions. The proposed method
consists of three stages: (1) the contrast-limited adaptive histogram
equalization (CLAHE) is applied to the original images and concatenated to the
extracted edges in order to create 4D images; (2) the patches of size 80 * 80 *
80 * 2 are randomly selected from the 4D images; and (3) the extracted patches
are passed into an attention-based CNN which is used to segment the lesions.
Finally, the proposed method was compared to previous studies of the same
dataset. Results: The current study evaluates the model, with a test set of
ISIB challenge data. Experimental results illustrate that the proposed approach
significantly surpasses existing methods in terms of Dice similarity and
Absolute Volume Difference while the proposed method use just one modality
(FLAIR) to segment the lesions. Conclusions: The authors have introduced an
automated approach to segment the lesions which is based on, at most, two
modalities as an input. The proposed architecture is composed of convolution,
deconvolution, and an SCA-VoxRes module as an attention module. The results
show, the proposed method outperforms well compare to other methods.
