Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI

• URL: http://arxiv.org/abs/2512.20436v1
• Date: Tue, 23 Dec 2025 15:24:31 GMT
• Title: Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI
• Authors: Muhammad Usman, Azka Rehman, Muhammad Mutti Ur Rehman, Abd Ur Rehman, Muhammad Umar Farooq,
• Abstract summary: We study ischemic stroke lesion segmentation using multimodal diffusion MRI from the ISLES 2022 dataset.<n>Several state-of-the-art convolutional and transformer-based architectures, including U-Net variants, Swin-UNet, and TransUNet, are benchmarked.<n>Results show that transformer-based models outperform convolutional baselines, and the proposed dual-encoder TransUNet achieves the best performance, reaching a Dice score of 85.4% on the test set.
• Score: 5.332404648315838
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate segmentation of ischemic stroke lesions from diffusion magnetic resonance imaging (MRI) is essential for clinical decision-making and outcome assessment. Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) scans provide complementary information on acute and sub-acute ischemic changes; however, automated lesion delineation remains challenging due to variability in lesion appearance. In this work, we study ischemic stroke lesion segmentation using multimodal diffusion MRI from the ISLES 2022 dataset. Several state-of-the-art convolutional and transformer-based architectures, including U-Net variants, Swin-UNet, and TransUNet, are benchmarked. Based on performance, a dual-encoder TransUNet architecture is proposed to learn modality-specific representations from DWI and ADC inputs. To incorporate spatial context, adjacent slice information is integrated using a three-slice input configuration. All models are trained under a unified framework and evaluated using the Dice Similarity Coefficient (DSC). Results show that transformer-based models outperform convolutional baselines, and the proposed dual-encoder TransUNet achieves the best performance, reaching a Dice score of 85.4% on the test set. The proposed framework offers a robust solution for automated ischemic stroke lesion segmentation from diffusion MRI.

Related papers

• Improved mmFormer for Liver Fibrosis Staging via Missing-Modality Compensation [8.687370165870613]
  We propose a multimodal MRI classification model based on the mmFormer architecture with an adaptive module for handling arbitrary combinations of missing modalities.<n>This method is evaluated on the test set of Comprehensive Analysis & Computing of REal-world medical images (CARE 2025 challenge)<n>For Cirrhosis Detection and Substantial Fibrosis Detection on in-distribution vendors, our model obtains accuracies of 66.67%, and 74.17%, and corresponding area under the curve (AUC) scores of 71.73% and 68.48%, respectively.
  arXiv  Detail & Related papers  (2025-09-19T21:31:05Z)
• DMCIE: Diffusion Model with Concatenation of Inputs and Errors to Improve the Accuracy of the Segmentation of Brain Tumors in MRI Images [0.9374652839580183]
  We propose DMCIE (Diffusion Model with Concatenation of Inputs and Errors), a novel framework for accurate brain tumor segmentation in MRI scans.<n>We employ a 3D U-Net to generate an initial segmentation mask, from which an error map is generated by identifying the differences between the prediction and the ground truth.<n>Using multimodal MRI inputs (T1, T1ce, T2, FLAIR), DMCIE effectively enhances segmentation accuracy by focusing on misclassified regions, guided by the original inputs.
  arXiv  Detail & Related papers  (2025-07-01T17:34:50Z)
• Deep Learning-Driven Segmentation of Ischemic Stroke Lesions Using Multi-Channel MRI [0.0]
  This study introduces a novel deep learning-based method for segmenting ischemic stroke lesions.<n>The proposed architecture integrates DenseNet121 as the encoder with Self-Organized Operational Neural Networks (ONN) in the decoder.<n>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.
  arXiv  Detail & Related papers  (2025-01-04T13:38:06Z)
• SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging [59.78761085714715]
  This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework for liver lesion classification. The proposed framework has been validated through comprehensive experiments on two clinical datasets. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public.
  arXiv  Detail & Related papers  (2024-02-27T06:32:56Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images [39.94162291765236]
  We present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map. We employ a diffusion model trained on healthy samples and combine Denoising Diffusion Probabilistic Model (DDPM) and Denoising Implicit Model (DDIM) at each step of the sampling process.
  arXiv  Detail & Related papers  (2023-08-03T21:56:50Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images [0.2578242050187029]
  Multiple Sclerosis (MS) is an autoimmune, and demyelinating disease that leads to lesions in the central nervous system. Up to now a multitude of multimodality automatic biomedical approaches is used to segment lesions. Authors propose a method employing just one modality (FLAIR image) to segment MS lesions accurately.
  arXiv  Detail & Related papers  (2022-01-05T21:37:43Z)
• Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction [0.0]
  Data Consistency (DC) is crucial for generalization in multi-modal data and robustness in detecting pathology. This work proposes the Cascades of Independently Recurrent Inference Machines (CIRIM) to assess DC through unrolled optimization. We show that the CIRIM performs best when implicitly enforcing DC, while the E2EVN requires explicitly formulated DC.
  arXiv  Detail & Related papers  (2021-11-30T15:34:30Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
  We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
  arXiv  Detail & Related papers  (2020-02-25T14:48:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.