# MS-DASPNet: Multiple Sclerosis lesion segmentation from brain MRI using dual attention and spatial pyramid pooling with transfer learning

## TL;DR

This paper introduces MS-DASPNet, a deep learning model that improves the accuracy of detecting and segmenting multiple sclerosis lesions in brain MRI scans.

## Contribution

The novel contribution is the use of dual attention modules and spatial pyramid pooling in a deep neural network for enhanced MS lesion segmentation.

## Key findings

- MS-DASPNet achieved a Dice score of 0.8736 on the MICCAI-2016 dataset.
- The model outperformed existing methods in Precision, Sensitivity, and Jaccard scores across multiple datasets.
- Dual attention modules improved spatial and channel-wise feature representation for lesion detection.

## Abstract

Accurate detection and segmentation of multiple sclerosis (MS) lesions in brain Magnetic Resonance Imaging (MRI) is a challenging task due to their small size, irregular shape, and variability in different imaging modalities. Precise segmentation of MS lesions from brain MRI is vital for early diagnosis, disease progression monitoring, and treatment planning. We introduce MS-DASPNet, a Dual Attention Guided Deep Neural Network specifically designed to address the challenges of MS lesion detection, including small lesion sizes, low contrast, and heterogeneous appearance. MS-DASPNet employs a VGG-16-based encoder, an Atrous Spatial Pyramid Pooling (ASPP) bottleneck for multi-scale context learning, and dual attention modules in each skip connection to simultaneously refine spatial details and enhance channel-wise feature representation. Evaluations on four publicly available datasets, namely ISBI-2015, Mendeley, MICCAI-2016, and MICCAI-2021, demonstrate that MS-DASPNet achieves superior Precision, Dice, Sensitivity, and Jaccard scores compared to state-of-the-art methods. MS-DASPNet attains a Dice score of 0.8736 on the MICCAI-2016 dataset and 0.8706 on the MICCAI-2021 dataset, both outperforming existing segmentation techniques, highlighting its robustness and effectiveness in accurate MS lesion segmentation.