Deep Learning-Driven Segmentation of Ischemic Stroke Lesions Using Multi-Channel MRI
Journal:
arXiv
Published Date:
Jan 4, 2025
Abstract
Ischemic stroke, caused by cerebral vessel occlusion, presents substantial
challenges in medical imaging due to the variability and subtlety of stroke
lesions. Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing
and managing ischemic stroke, yet existing segmentation techniques often fail
to accurately delineate lesions. This study introduces a novel deep
learning-based method for segmenting ischemic stroke lesions using
multi-channel MRI modalities, including Diffusion Weighted Imaging (DWI),
Apparent Diffusion Coefficient (ADC), and enhanced Diffusion Weighted Imaging
(eDWI). The proposed architecture integrates DenseNet121 as the encoder with
Self-Organized Operational Neural Networks (SelfONN) in the decoder, enhanced
by Channel and Space Compound Attention (CSCA) and Double
Squeeze-and-Excitation (DSE) blocks. Additionally, a custom loss function
combining Dice Loss and Jaccard Loss with weighted averages is introduced to
improve model performance. Trained and evaluated on the ISLES 2022 dataset, the
model achieved Dice Similarity Coefficients (DSC) of 83.88% using DWI alone,
85.86% with DWI and ADC, and 87.49% with the integration of DWI, ADC, and eDWI.
This approach not only outperforms existing methods but also addresses key
limitations in current segmentation practices. These advancements significantly
enhance diagnostic precision and treatment planning for ischemic stroke,
providing valuable support for clinical decision-making.
