AugMS-Net:Augmented multiscale network for small cervical tumor segmentation from MRI volumes

Abstract

Cervical cancer is one of the leading causes of female-specific cancer death. Tumor region segmentation plays a pivotal role in both the clinical analysis and treatment planning of cervical cancer. Due to the heterogeneity and low contrast of biomedical images, current state-of-the-art tumor segmentation approaches are facing the challenge of the insensitive detection of small lesion regions. To tackle this problem, this paper proposes an augmented multiscale network (AugMS-Net) based on 3D U-Net to automatically segment cervical Magnetic Resonance Imaging (MRI) volumes. Since a multiscale strategy is considered one of the most promising algorithms to tackle small object recognition, we introduce a novel 3D module to explore more granular multiscale representations. Besides, we employ a deep multiscale supervision strategy to doubly supervise the side outputs hierarchically. To demonstrate the generalization of our model, we evaluated AugMS-Net on both a cervical dataset from MRI volumes and a liver dataset from Computerized Tomography (CT) volumes. Our proposed AugMS-Net shows superior performance over baseline models, yielding high accuracy while reducing the number of model parameters by nearly 20%. The source code and trained models are available at https://github.com/Cassieyy/AugMS-Net.

Keywords: Augmented multiscale; Biomedical image; Deep learning; Semantic segmentation.