kCBAC-Net: Deeply Supervised Complete Bipartite Networks with Asymmetric Convolutions for Medical Image Segmentation

Accurate and automatic medical image segmentation is challenging due to significant size and shape variations of objects (e.g., in multi-scales) and missing/blurring object borders. In this paper, we propose a new deeply supervised k-complete-bipartite network with asymmetric convolutions (kCBAC-Net) to exploit multi-scale features and improve the capability of standard convolutions for segmentation. (1) We leverage a generalized complete bipartite network to reuse multi-scale features, consolidate feature hierarchies at different scales, and preserve maximum information flow between encoder and decoder layers. (2) To further capture multi-scale information, we sequentially connect k complete bipartite network modules together to facilitate their processing in different image scales. (3) We replace the standard convolution by asymmetric convolution block to strengthen the central skeleton parts of standard convolution, enhancing the model's robustness on exploiting more discriminative features. (4) We employ auxiliary deep supervisions to boost information flow in the network and extract highly discriminative features. We evaluate our kCBAC-Net on three datasets (ultrasound lymph node segmentation (2D), 2017 ISIC Skin Lesion segmentation (2D), and MM-WHS CT (3D)), achieving state-of-the-art performance.