  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.
