Zhang Xiaodong, Zhang Yongquan, Wang Changmiao, Li Lin, Zhu Fengjun, Sun Yang, Mo Tong, Hu Qingmao, Xu Jinping, Cao Dezhi
Shenzhen Children's Hospital, Shenzhen, 518000, Guangdong, China.
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518000, Guangdong, China.
Insights Imaging. 2024 Sep 12;15(1):222. doi: 10.1186/s13244-024-01803-8.
Accurate segmentation of focal cortical dysplasia (FCD) lesions from MR images plays an important role in surgical planning and decision but is still challenging for radiologists and clinicians. In this study, we introduce a novel transformer-based model, designed for the end-to-end segmentation of FCD lesions from multi-channel MR images.
The core innovation of our proposed model is the integration of a convolutional neural network-based encoder-decoder structure with a multiscale transformer to augment the feature representation of lesions in the global field of view. Transformer pathways, composed of memory- and computation-efficient dual-self-attention modules, leverage feature maps from varying depths of the encoder to discern long-range interdependencies among feature positions and channels, thereby emphasizing areas and channels relevant to lesions. The proposed model was trained and evaluated on a public-open dataset including MR images of 85 patients using both subject-level and voxel-level metrics.
Experimental results indicate that our model offers superior performance both quantitatively and qualitatively. It successfully identified lesions in 82.4% of patients, with a low false-positive lesion cluster rate of 0.176 ± 0.381 per patient. Furthermore, the model achieved an average Dice coefficient of 0.410 ± 0.288, outperforming five established methods.
Integration of the transformer could enhance the feature presentation and segmentation performance of FCD lesions. The proposed model has the potential to serve as a valuable assistive tool for physicians, enabling rapid and accurate identification of FCD lesions. The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET .
This multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations of focal cortical dysplasia patients from MR images.
The first transformer-based model was built to explore focal cortical dysplasia lesion segmentation. Integration of global and local features enhances the segmentation performance of lesions. A valuable benchmark for model development and comparative analyses was provided.
从磁共振图像中准确分割局灶性皮质发育异常（FCD）病变在手术规划和决策中起着重要作用，但对放射科医生和临床医生来说仍然具有挑战性。在本研究中，我们引入了一种基于新型变换器的模型，用于从多通道磁共振图像中对FCD病变进行端到端分割。
我们提出的模型的核心创新点是将基于卷积神经网络的编码器-解码器结构与多尺度变换器相结合，以增强病变在全局视野中的特征表示。由内存和计算效率高的双自注意力模块组成的变换器路径利用来自编码器不同深度的特征图来识别特征位置和通道之间的长程相互依赖关系，从而突出与病变相关的区域和通道。所提出的模型在一个公开数据集上进行训练和评估，该数据集包括85名患者的磁共振图像，使用了受试者水平和体素水平的指标。
实验结果表明，我们的模型在定量和定性方面都具有卓越的性能。它成功地在82.4%的患者中识别出病变，每位患者的假阳性病变簇率低至0.176±0.381。此外，该模型的平均Dice系数为0.410±0.288，优于五种已有的方法。
变换器的集成可以增强FCD病变的特征呈现和分割性能。所提出的模型有可能作为医生的有价值辅助工具，能够快速准确地识别FCD病变。源代码和预训练模型权重可在https://github.com/zhangxd0530/MS-DSA-NET获取。
这种基于多尺度变换器的模型执行局灶性皮质发育异常病变的分割，旨在帮助放射科医生和临床医生从磁共振图像中对局灶性皮质发育异常患者进行准确有效的术前评估。
构建了第一个基于变换器的模型来探索局灶性皮质发育异常病变分割。全局和局部特征的集成增强了病变的分割性能。提供了一个用于模型开发和比较分析的有价值基准。
