Towards better semantic consistency of 2D medical image segmentation

The latest deep neural networks for medical segmentation typically utilize transposed convolutional filters and atrous convolutional filters for spatial restoration and larger receptive fields, leading to dilution and inconsistency of visual semantics. To address such issues, we propose a novel attentional up-concatenation structure to build an auxiliary path for direct access to multi-level features. In addition, we employ a new structural loss to bring better morphological awareness and reduce the segmentation flaws caused by the semantic inconsistencies. Thorough experiments on the challenging optic cup/disc segmentation, cellular segmentation and lung segmentation tasks were performed to evaluate the proposed methods. Further ablation analysis demonstrated the effectiveness of the different components of the model and illustrated its efficiency. The proposed methods achieved the best performance and speed compared to the state-of-the-art models in three tasks on seven public datasets, including DRISHTI-GS, RIM-r3, REFUGE, MESSIDOR, TNBC, GlaS and LUNA.