Abstract
Accurate and automated segmentation of ischemic stroke lesions from magnetic resonance imaging (MRI) data is crucial for rapid clinical diagnosis, treatment planning, and patient prognosis. This study presents a comprehensive evaluation of various deep learning architectures for segmenting acute and subacute ischemic lesions using the multicenter ISLES 2022 dataset. We compare traditional 3D U-Net models, attention mechanisms, and modern hybrid architectures, specifically Swin-UNETR (Swin Transformer-based UNet TRansformer). Our experiments utilize multimodal MRI data, including diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps. Results demonstrate that the Swin-UNETR architecture, combined with a specialized pad or crop preprocessing pipeline, advanced data augmentation using the TorchIO library, and a combined Dice–Cross Entropy (DiceCE) loss function, provides competitive performance. The best Swin-UNETR model achieved a Dice of 0.717, precision of 0.793, recall of 0.708, and HD95 of 12.24 mm on the validation set, with a loss of 0.26. The baseline 3D U-Net achieved a comparable Dice of 0.726, while Swin-UNETR showed higher precision, recall, and lower HD95. This study highlights the effectiveness of integrating hierarchical transformers with convolutional decoders for extracting both local and global contextual information in complex medical imaging tasks.