Pham Tien Viet, Vu Tu Ngoc, Le Hoang-Minh-Quang, Pham Van-Truong, Tran Thi-Thao
Department of Automation Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam.
J Imaging Inform Med. 2025 Feb;38(1):94-123. doi: 10.1007/s10278-024-01191-x. Epub 2024 Jul 9.
Deep neural networks have shown excellent performance in medical image segmentation, especially for cardiac images. Transformer-based models, though having advantages over convolutional neural networks due to the ability of long-range dependence learning, still have shortcomings such as having a large number of parameters and and high computational cost. Additionally, for better results, they are often pretrained on a larger data, thus requiring large memory size and increasing resource expenses. In this study, we propose a new lightweight but efficient model, namely CapNet, based on convolutions and mixing modules for cardiac segmentation from magnetic resonance images (MRI) that can be trained from scratch with a small amount of parameters. To handle varying sizes and shapes which often occur in cardiac systolic and diastolic phases, we propose attention modules for pooling, spatial, and channel information. We also propose a novel loss called the Tversky Shape Power Distance function based on the shape dissimilarity between labels and predictions that shows promising performances compared to other losses. Experiments on three public datasets including ACDC benchmark, Sunnybrook data, and MS-CMR challenge are conducted and compared with other state of the arts (SOTA). For binary segmentation, the proposed CapNet obtained the Dice similarity coefficient (DSC) of 94% and 95.93% for respectively the Endocardium and Epicardium regions with Sunnybrook dataset, 94.49% for Endocardium, and 96.82% for Epicardium with the ACDC data. Regarding the multiclass case, the average DSC by CapNet is 93.05% for the ACDC data; and the DSC scores for the MS-CMR are 94.59%, 92.22%, and 93.99% for respectively the bSSFP, T2-SPAIR, and LGE sequences of the MS-CMR. Moreover, the statistical significance analysis tests with p-value compared with transformer-based methods and some CNN-based approaches demonstrated that the CapNet, though having fewer training parameters, is statistically significant. The promising evaluation metrics show comparative results in both Dice and IoU indices compared to SOTA CNN-based and Transformer-based architectures.
深度神经网络在医学图像分割中表现出色，尤其是在心脏图像方面。基于Transformer的模型虽然由于具有长距离依赖学习能力而优于卷积神经网络，但仍存在参数数量多、计算成本高的缺点。此外，为了获得更好的结果，它们通常在更大的数据上进行预训练，因此需要大内存并增加资源开销。在本研究中，我们提出了一种新的轻量级但高效的模型，即CapNet，它基于卷积和混合模块，用于从磁共振图像（MRI）中进行心脏分割，该模型可以用少量参数从头开始训练。为了处理心脏收缩期和舒张期经常出现的不同大小和形状，我们提出了用于池化、空间和通道信息的注意力模块。我们还提出了一种基于标签和预测之间形状差异的新型损失函数，称为Tversky形状功率距离函数，与其他损失函数相比，该函数显示出良好的性能。在包括ACDC基准、Sunnybrook数据和MS-CMR挑战在内的三个公共数据集上进行了实验，并与其他现有技术（SOTA）进行了比较。对于二值分割，在Sunnybrook数据集中，所提出的CapNet在内膜和外膜区域分别获得了94%和95.93%的Dice相似系数（DSC）；在ACDC数据中，内膜的DSC为94.49%，外膜的DSC为96.82%。对于多类情况，CapNet在ACDC数据上的平均DSC为93.05%；在MS-CMR中，bSSFP、T2-SPAIR和LGE序列的DSC分数分别为94.59%、92.22%和93.99%。此外，与基于Transformer的方法和一些基于CNN的方法相比，p值的统计显著性分析测试表明，CapNet虽然训练参数较少，但具有统计学意义。与基于SOTA的CNN和Transformer架构相比，这些有前景的评估指标在Dice和IoU指数方面都显示出了比较结果。
