Medical image segmentation using customized U-Net with adaptive activation functions

Abstract

Since medical imaging is a fundamental step in clinical diagnosis and treatment, medical image processing is an attractive field for researchers. Among the different applications of medical image processing, this paper focuses on the segmentation task using a customized deep convolutional neural network (CNN). The proposed network is developed based on the idea of improving a deep network performance and speeding up its learning process while using less parameters. Using the famous U-Net architecture which has proven its effectiveness in the segmentation field, the customization is done here by applying adaptive activation functions. In the proposed network, the U-Net complexity is reduced about 200 times by alleviating some parameters to accelerate the learning process. However, the important modification is the use of adaptive activation functions where each convolution layer learns its own data-adaptive activation function as a linear combination of 16 well-known basic functions. This modification successfully compensates the accuracy drop caused by parameter alleviation and also makes the model capable to be tuned with small amount of training data. Conducting several experiments on five famous retinal image datasets, namely DRIVE, STARE, CHASE, HRF, and ARIA, the proposed customized U-Net achieved 96%, 97%, 96%, 97%, and 95% accuracy in segmenting blood vessels, respectively. The proposed network also showed 98% accuracy on ISIC skin lesion dataset for segmenting the lesion area. The obtained results obviously confirm the high performance of the proposed customized network compared to the previous successful researches in handling the medical segmentation task. They also light the hope that many famous deep networks can benefit from these types of customization to become efficient compact models with the ability to handle lack of sufficient data.