Automated Segmentation of Acute Ischemic Stroke Using Attention U-net with Patch Mechanism

Ischemic stroke, a widespread neurological condition with a substantial mortality rate, necessitates accurate delineation of affected regions to enable proper evaluation of patient outcomes. However, such precision is complicated by factors like variable lesion sizes, noise interference, and the overlapping intensity characteristics of different tissue structures. This research addresses these issues by focusing on the segmentation of Diffusion Weighted Imaging (DWI) scans from the ISLES 2022 dataset and conducting a comparative assessment of three advanced deep learning models: the U-Net framework, its U-Net++ extension, and the Attention U-Net. Applying consistent evaluation criteria specifically, Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and recall the Attention U-Net emerged as the superior choice, establishing record high values for IoU (0.8223) and DSC (0.9021). Although U-Net achieved commendable recall, its performance lagged behind that of U-Net++ in other critical measures. These findings underscore the value of integrating attention mechanisms to achieve more precise segmentation. Moreover, they highlight that the Attention U-Net model is a reliable candidate for medical imaging tasks where both accuracy and efficiency hold paramount importance, while U Net and U Net++ may still prove suitable in certain niche scenarios.

The acute ischemic stroke (AIS) impacts extensively all over the world, the early diagnosis can provide valuable property information of disease. However, it's difficult for our human eyes to distinguish the fine pathological changes. Here we introduce self-attention mechanisms and propose UCATR, an NCCT image segmentation network for AIS lesions. It uses the advantages of Transformer to effectively learn the global context features of the image, and is based on convolutional neural network (CNN) and Transformer as the encoder, adding Multi-Head Cross-Attention (MHCA) modules to the decoder to achieve high-precision spatial information recovery. This method is experimentally verified on the NCCT dataset of AIS provided by Chengdu Medical College in China to obtain that the Dice similarity coefficient of lesion segmentation is 73.58%, which is better than U-Net, Attention U-Net and TransUNet. Furthermore, we conduct ablation study on the MHCA module at three different positions in the decoder to prove its efficiency.

Stroke is brain cell death because of either lack of blood flow (ischemic) or bleeding (hemorrhagic) that prevents the brain from functioning properly in both conditions. Ischemic stroke is a common type of stroke caused by a blockage in the cerebrovascular system that prevents blood from flowing to brain regions and directly blocks blood vessels. Computed tomography (CT) scanning is frequently used in the evaluation of stroke, and rapid and accurate diagnosis of ischemic stroke with CT images is critical for determining the appropriate treatment. The manual diagnosis of ischemic stroke can be error-prone due to several factors, such as the busy schedules of specialists and the large number of patients admitted to healthcare facilities. Therefore, in this paper, a deep learning-based interface was developed to automatically diagnose the ischemic stroke through segmentation on CT images leading to a reduction on the diagnosis time and workload of specialists. Convolutional Neural Networks (CNNs) allow automatic feature extraction in ischemic stroke segmentation, utilized to mark the disease regions from CT images. CNN-based architectures, such as U-Net, U-Net VGG16, U-Net VGG19, Attention U-Net, and ResU-Net, were used to benchmark the ischemic stroke disease segmentation. To further improve the segmentation performance, ResU-Net was modified, adding a dilation convolution layer after the last layer of the architecture. In addition, data augmentation was performed to increase the number of images in the dataset, including the ground truths for the ischemic stroke disease region. Based on the experimental results, our modified ResU-Net with a dilation convolution provides the highest performance for ischemic stroke segmentation in dice similarity coefficient (DSC) and intersection over union (IoU) with 98.45 % and 96.95 %, respectively. The experimental results show that our modified ResU-Net outperforms the state-of-the-art approaches for ischemic stroke disease segmentation. Moreover, the modified architecture has been deployed into a new desktop application called BrainSeg, which can support specialists during the diagnosis of the disease by segmenting ischemic stroke.

Computed tomography (CT) is the first-line imaging modality for evaluation of patients suspected of stroke. Specially, such modality is key as screening test between ischemia and hemorrhage strokes. Despite remarkable support of encoder-decoder architectures, the delineation of ischemic lesions remains challenging on CT studies, reporting poor sensitivity, especially in the acute stage. Among others, these nets are affected because of the low scan quality, the challenging stroke geometry, and the variable textural representation. This work introduces a boundary-focused attention U-Net that takes advantage of cross-attention mechanism, that along multiple levels allows to recover stroke segmentation on CT scans. The proposed architecture is enriched with skip connections, that help in the recovering of saliency lesion maps and motivated the preservation of morphology. Besides, an auxiliary class is herein introduced with a weighted special loss function that remark lesion tissue, alleviating the negative impact of class unbalance. The proposed approach was validated on the public ISLES2018 dataset achieving an average dice score of 0.42 and a precision of 0.48.

Segmentation of lesions is crucial for the detection and treatment of ischemic stroke. The aim of this work is to develop a robust and highly accurate framework for lesion stroke detection in brain Magnetic Resonance Imaging (MRI). The paper propose a novel deep learning model, named EnigmaNet, for the segmentation of ischemic stroke lesions in Fluid-Attenuated Inversion Recovery (FLAIR) and Diffusion Weighted Imaging (DWI) images. EnigmaNet use novel Genesis-k blocks and dual attention mechanism in the encoder and decoder blocks of the architecture. A modified Weighted Focal-Tversky-Dice (wFTD) Loss is used for improved performance. The model is validated on the ISLES-2015 public dataset. EnigmaNet showed a Dice score of 0.8965, sensitivity of 0.8776 and specificity of 0.9866 for the FLAIR test images. Dice score of 0.8423, sensitivity of 0.8452 and specificity of 0.9754 were obtained for DWI images. The segmentation results of EnigmaNet shows an improvement in Dice score of about 32% over U-Net-sharp, 41% over FCN-8 and 10% over Attention U-Net. A region-based comparison of segmentation results of EnigmaNet highlighted its ability to detect fine lesions accurately in multiple vascular territories in brain, thereby signifying its robustness to segment lesions of diverse size, shape and location. The proposed model showed improved results as compared to the state-of-the-art techniques. EnigmaNet model is thus a promising approach for accurate and robust segmentation of ischemic stroke lesions.

Data privacy considerations and data fragmentation between healthcare institutions is causing segmentation of ischemic stroke lesions from neuroimaging to be hindered. The centralized approach may conflict with HIPAA and GDPR, and the federated learning approach does not have proper privacy guarantees nor is it able to capture lesion detail. This work prposes Fed-AttUNet-DP,a three major contributions: a spatial attention mechanism to integrate federated learning for better stroke lesion detection; differential privacy at the client level and secure MPCC at the server level for dual-layered privacy preservation; adaptive federated optimization for non-IID medical data and faster training speed and better performance. The proposed framework is an augmentation to the federated averaging algorithm which exploits attention U-Net and antennas attention (differential privacy i.e. gradient clipping and Gaussian noise). Ten simulated health care institutions were involved in 150 rounds of communication, at a rate of [Formula: see text] participation of the clients. Privacy budget was configured set at [Formula: see text], [Formula: see text] and a multiplier of noise [Formula: see text] as well as gradient clipping threshold [Formula: see text]. The distance between data heterogeneity was measured with Earth Mover (EMD [Formula: see text]). Experiments are conducted on the BRISC2025 dataset. The Dice similarity coefficient and IoU for Fed-AttUNet-DP is 0.930 and 0.890 respectively, sensitivity is 0.941 and specificity is 0.982. It beats all federated baselines by only [Formula: see text] accuracy drop compared to centralized training as well as [Formula: see text] compared to local-only training. The inference per volume is 0.8 s, converges the system with 5.18 GB total communication cost with 150 rounds. It is verified in the works of the ablation research that attention gates ([Formula: see text] DSC), differential privacy ([Formula: see text] DSC trade-off), and secure aggregation individually contribute to it. Our work provides formal privacy guarantees and advances segmentation accuracy, by mitigating non-IID heterogeneity across multiple medical image institutions and respecting HIPAA/GDPR regulations.

Manual segmentation of sub-acute ischemic stroke lesions in fluid-attenuated inversion recovery magnetic resonance imaging (FLAIR MRI) is time-consuming and subject to inter-observer variability, limiting clinical workflow efficiency. To develop and validate an automated deep learning framework for accurate segmentation of sub-acute ischemic stroke lesions in FLAIR MRI using rigorous validation methodology. We propose a novel multi-path residual U-Net(U-shaped network) architecture with six parallel pathways per block (depths 0–5 convolutional layers) and 2.34 million trainable parameters. Hyperparameters were systematically optimized using 5-fold cross-validation across 60 configurations. We addressed intensity inhomogeneity using N4 bias field correction and employed strict patient-level data partitioning (18 training, 5 validation, 5 test patients) to prevent data leakage. Statistical analysis utilized bias-corrected bootstrap confidence intervals and Bonferroni correction for multiple comparisons. Our model achieved a validation dice similarity coefficient (DSC) of 0.85 ± 0.12 (95% CI: 0.79–0.91), a sensitivity of 0.82 ± 0.15, a specificity of 0.95 ± 0.04, and a Hausdorff distance of 14.1 ± 5.8 mm. Test set performance remained consistent (DSC: 0.89 ± 0.07), confirming generalizability. Computational efficiency was demonstrated with 45 ms inference time per slice. The architecture demonstrated statistically significant improvements over DRANet (p = 0.003), 2D CNN (p = 0.001), and Attention U-Net (p = 0.001), while achieving competitive performance comparable to CSNet (p = 0.68). The proposed framework demonstrates robust performance for automated stroke lesion segmentation with rigorous statistical validation. However, multi-site validation across diverse clinical environments remains essential before clinical implementation.

Stroke was the cause of one out of every six deaths from cerebrovascular disease in 2020. A stroke occurs in the United States (US) every 40 seconds. Every 3.5 minutes, people die of a stroke. More than total 795,000 stroke cases occur yearly in the US. This study aims to detect the ischemic stroke lesion that occurs in the brain. The Ischemic Stroke Lesion Segmentation (ISLES) 2017 data set, which includes 82 Magnetic Resonance images of 43 patients, was used. The UNet, Attention UNet, Residual UNet, Attention Residual UNet, and Residual UNet++ segmentation networks were tested. Moreover, Cross Entropy, Dice, IoU, Tversky, Focal Tversky, and their compound forms were analyzed. The IoU loss function tested on Attention UNet achieved the best performance with the dice score of 0.766, the IoU score of 0.621, the sensitivity of 0.730, the specificity of 0.997, the precision of 0.805, and the accuracy of 0.993.

Ischemic stroke lesion segmentation in MRI images represents significant challenges, particularly due to class imbalance between foreground and background pixels. Several approaches have been developed to achieve higher F1-Scores in stroke lesion segmentation under this challenge. These strategies include convolutional neural networks (CNN) and models that represent a large number of parameters, which can only be trained on specialized computational architectures that are explicitly oriented to data processing. This paper proposes a lightweight model based on the U-Net architecture that handles an attention module and the Generalized Dice Focal loss function to enhance the segmentation accuracy in the class imbalance environment, characteristic of stroke lesions in MRI images. This study also analyzes the segmentation performance according to the pixel size of stroke lesions, giving insights into the loss function behavior using the public ISLES 2015 and ISLES 2022 MRI datasets. The proposed model can effectively segment small stroke lesions with F1-Scores over 0.7, particularly in FLAIR, DWI, and T2 sequences. Furthermore, the model shows reasonable convergence with their 7.9 million parameters at 200 epochs, making it suitable for practical implementation on mid and high-end general-purpose graphic processing units.

Correction to: 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association.

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Marc Fisher MD Kennedy Lees MD Section Editors: On September 26, 2008, the New England Journal of Medicine published the results of the European Cooperative Stroke Study (ECASS) III,1 the first randomized, placebo-controlled trial to demonstrate safe and effective use of intravenous recombinant tissue plasminogen activator (rtPA) to treat patients with acute ischemic stroke (AIS) beyond 3 hours from stroke onset. The ECASS investigators studied the safety and efficacy of administering intravenous rtPA to patients with AIS 3 to 4.5 hours after AIS onset. Using the modified Rankin Scale score at 90 days after stroke occurrence as the primary end point of the study, the investigators demonstrated a modest, statistically significant increase in the likelihood of having normal or near normal recovery (modified Rankin Scale=0 or 1) in favor of rtPA treatment compared with placebo (unadjusted OR, 1.34; 95% CI, 1.02 to 1.76; P =0.04). So, what impact will the results of the study have on acute stroke management and stroke research in the United States and elsewhere? With regard to the first part of the question, the answer is complex. First, the ECASS III results will hopefully help to increase the number of thrombolysis eligible patients with AIS who receive rtPA. Twelve years after the US Food and Drug Administration approved the management of AIS within 3 hours of symptom onset as an indication for the use of intravenous rtPA, less than 5% of patients with AIS are being treated worldwide with rtPA within 3 hours of stroke onset. One of the major factors contributing to this parlous state of affairs has been disagreement among healthcare professionals about the validity of the results of the National Institutes of Neurological Disorders and Stroke (NINDS) trial of rtPA for acute stroke.2 In the late 1990s, the stroke community unexpectedly …

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