Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain.
Cardiovascular Unit. Ascires Biomedical Group., Valencia, Spain.
Comput Med Imaging Graph. 2022 Jul;99:102085. doi: 10.1016/j.compmedimag.2022.102085. Epub 2022 Jun 3.
The correct assessment and characterization of heart anatomy and functionality is usually done through inspection of magnetic resonance image cine sequences. In the clinical setting it is especially important to determine the state of the left ventricle. This requires the measurement of its volume in the end-diastolic and end-systolic frames within the sequence trough segmentation methods. However, the first step required for this analysis before any segmentation is the detection of the end-systolic and end-diastolic frames within the image acquisition. In this work we present a fully convolutional neural network that makes use of dilated convolutions to encode and process the temporal information of the sequences in contrast to the more widespread use of recurrent networks that are usually employed for problems involving temporal information. We trained the network in two different settings employing different loss functions to train the network: the classical weighted cross-entropy, and the weighted Dice loss. We had access to a database comprising a total of 397 cases. Out of this dataset we used 98 cases as test set to validate our network performance. The final classification on the test set yielded a mean frame distance of 0 for the end-diastolic frame (i.e.: the selected frame was the correct one in all images of the test set) and 1.242 (relative frame distance of 0.036) for the end-systolic frame employing the optimum setting, which involved training the neural network with the Dice loss. Our neural network is capable of classifying each frame and enables the detection of the end-systolic and end-diastolic frames in short axis cine MRI sequences with high accuracy.
心脏解剖结构和功能的正确评估和描述通常是通过检查磁共振图像电影序列来完成的。在临床环境中，确定左心室的状态尤为重要。这需要通过分段方法测量序列中舒张末期和收缩末期帧中的心室容积。然而，在进行任何分段之前，这种分析所需的第一步是在图像采集过程中检测收缩末期和舒张末期帧。在这项工作中，我们提出了一种完全卷积神经网络，该网络利用扩张卷积来编码和处理序列的时间信息，而不是更广泛使用的递归网络，递归网络通常用于涉及时间信息的问题。我们在两种不同的设置中训练了网络，使用不同的损失函数来训练网络：经典的加权交叉熵和加权 Dice 损失。我们可以访问一个包含总共 397 个病例的数据库。在这个数据集上，我们使用了 98 个病例作为测试集来验证我们网络的性能。在测试集上的最终分类中，对于舒张末期帧，网络的平均帧距离为 0（即：在测试集中的所有图像中，选择的帧都是正确的），而对于收缩末期帧，最优设置下的平均帧距离为 1.242（相对帧距离为 0.036），最优设置涉及使用 Dice 损失训练神经网络。我们的神经网络能够对每帧进行分类，并能够以高精度检测短轴电影 MRI 序列中的收缩末期和舒张末期帧。
