Chan-Vese Attention U-Net: An attention mechanism for robust segmentation
This work addresses the need for more reliable segmentation in medical imaging, though it appears incremental as it builds on existing U-Net architectures.
The authors tackled the problem of unreliable segmentation in convolutional neural networks by proposing a new attention gate based on Chan-Vese energy minimization to constrain segmentation masks, achieving competitive results in binary segmentation on MRI brain images.
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.