MMS-Net: Multi-level multi-scale feature extraction network for medical image segmentation

Abstract

Automatic segmentation of medical images can provide a reliable basis for clinical analysis and disease diagnosis. Due to the varying sizes and appearances of different pathological lesions, most convolutional neural networks (CNNs) approaches are unable to fully extract multi-scale features and usually lead to false positive and false negative segmentation. Multi-scale medical image segmentation still faces significant challenges. By designing different levels of skip connection modules (SCMs), local multi-scale context is proposed. We also adopted feature pyramid network (FPN) to connect SCMs to extract global multi-scale features. Local multi-scale context and global multi-scale feature constitute a multi-level multi-scale feature extraction network (MMS-Net). Multi-level multi-scale feature was extracted and accurate spatial information was also retained. Comprehensive experiments (training vs. testing: 8:2) on five public datasets show that the proposed MMS-Net achieved accurate segmentation and outperformed the state-of-the-art algorithms. Besides, when only using 50% and 20% of the training dataset, MMS-Net still obtained competitive segmentation results.