LEACS: a learnable and efficient active contour model with space-frequency pooling for medical image segmentation

Diseases can be diagnosed and monitored by extracting regions of interest (ROIs) from medical images. However, accurate and efficient delineation and segmentation of ROIs in medical images remain challenging due to unrefined boundaries, inhomogeneous intensity and limited image acquisition. To overcome these problems, we propose an end-to-end learnable and efficient active contour segmentation model, which integrates a global convex segmentation (GCS) module into a light-weighted encoder-decoder convolutional segmentation network with a multiscale attention module (ED-MSA). The GCS automatically obtains the initialization and corresponding parameters of the curve deformation according to the prediction map generated by the ED-MSA, while provides the refined object boundary prediction for ED-MSA optimization. To provide precise and reliable initial contour for the GCS, we design the space-frequency pooling operation layers in the encoder stage of ED-MSA, which can effectively reduce the number of iterations of the GCS. Beside, we construct ED-MSA using the depth-wise separable convolutional residual module to mitigate the overfitting of the model. The effectiveness of our method is validated on four challenging medical image datasets. Code is here:https://github.com/Yang-fashion/ED-MSA_GCS.