When studying the results of a segmentation algorithm using convolutional neural networks, one wonders about the reliability and consistency of the results. This leads to questioning the possibility of using such an algorithm in applications where there is little room for doubt. We propose in this paper a new attention gate based on the use of Chan-Vese energy minimization to control more precisely the segmentation masks given by a standard CNN architecture such as the U-Net model. This mechanism allows to obtain a constraint on the segmentation based on the resolution of a PDE. The study of the results allows us to observe the spatial information retained by the neural network on the region of interest and obtains competitive results on the binary segmentation. We illustrate the efficiency of this approach for medical image segmentation on a database of MRI brain images.

翻译：在研究使用卷积神经网络的语义分割算法结果时，人们会质疑这些结果的可靠性与一致性。这引出了一个核心问题：在容错空间极小的应用场景中，能否使用此类算法？本文提出一种基于Chan-Vese能量最小化的新型注意力门控机制，旨在更精确地控制由标准CNN架构（如U-Net模型）生成的分割掩膜。该机制通过求解偏微分方程来约束分割过程。结果分析表明，该机制能使神经网络保留感兴趣区域的空间信息，并在二值分割任务中取得具有竞争力的结果。我们通过脑部MRI图像数据库验证了该方法在医学图像分割中的有效性。