Jazzar Nesrine, Mabrouk Besma, Douik Ali
Research Laboratory: Networked Objects, Control and Communication Systems, NOCCS-ENISo, National Engineering School of Sousse, University of Sousse, Soussse 4023, Tunisia.
National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia.
J Imaging. 2024 Nov 25;10(12):304. doi: 10.3390/jimaging10120304.
We propose a novel architecture, Transformer Dil-DenseUNet, designed to address the challenges of accurately segmenting stroke lesions in MRI images. Precise segmentation is essential for diagnosing and treating stroke patients, as it provides critical spatial insights into the affected brain regions and the extent of damage. Traditional manual segmentation is labor-intensive and error-prone, highlighting the need for automated solutions. Our Transformer Dil-DenseUNet combines DenseNet, dilated convolutions, and Transformer blocks, each contributing unique strengths to enhance segmentation accuracy. The DenseNet component captures fine-grained details and global features by leveraging dense connections, improving both precision and feature reuse. The dilated convolutional blocks, placed before each DenseNet module, expand the receptive field, capturing broader contextual information essential for accurate segmentation. Additionally, the Transformer blocks within our architecture address CNN limitations in capturing long-range dependencies by modeling complex spatial relationships through multi-head self-attention mechanisms. We assess our model's performance on the Ischemic Stroke Lesion Segmentation Challenge 2015 (SISS 2015) and ISLES 2022 datasets. In the testing phase, the model achieves a Dice coefficient of 0.80 ± 0.30 on SISS 2015 and 0.81 ± 0.33 on ISLES 2022, surpassing the current state-of-the-art results on these datasets.
我们提出了一种新颖的架构——Transformer Dil-DenseUNet，旨在应对在MRI图像中精确分割中风病灶的挑战。精确分割对于中风患者的诊断和治疗至关重要，因为它能提供有关受影响脑区和损伤程度的关键空间信息。传统的手动分割劳动强度大且容易出错，这凸显了对自动化解决方案的需求。我们的Transformer Dil-DenseUNet结合了DenseNet、空洞卷积和Transformer模块，每个部分都有独特的优势，有助于提高分割精度。DenseNet组件通过利用密集连接来捕捉细粒度细节和全局特征，提高了精度和特征重用性。置于每个DenseNet模块之前的空洞卷积块扩大了感受野，捕捉到准确分割所需的更广泛的上下文信息。此外，我们架构中的Transformer模块通过多头自注意力机制对复杂空间关系进行建模，解决了卷积神经网络在捕捉长距离依赖关系方面的局限性。我们在2015年缺血性中风病灶分割挑战赛（SISS 2015）和ISLES 2022数据集上评估了我们模型的性能。在测试阶段，该模型在SISS 2015上的Dice系数为0.80±0.30，在ISLES 2022上为0.81±0.33，超过了这些数据集上目前的最优结果。
