ACE-Net: Adaptive Context Extraction Network for Medical Image Segmentation

Tuo Leng, Yu Wang, Ying Li, Zhijie Wen

Abstract: It remains a challenging task to segment the medical images due to their diversity of structures. Although some state-of-the-art approaches have been proposed, the following two problems have not been fully explored: the redundant use of low-level features, and the lack of effective contextual modules to model long-range dependencies. In this paper, we propose a combination model based on ACE-Net of two newly proposed modules: The Joint Attention Upsample (JAU) module and Context Similarity Module (CSM). We extend skip connections by introducing an attention mechanism within the JAU module, followed by generating guidance information to weight low-level features using high-level features. We then introduce an affinity matrix into the CSM to optimize the long-range dependencies adaptively, which is based on a self-attention mechanism. Furthermore, our ACE-Net adaptively construct multi-scale contextual representations with multiple well-designed Context Similarity Modules (CSMs) which are been used in parallel in next process. Based on the evaluation on two public medical image datasets (EndoScene and RIM-ONE-R1), our network demonstrates significantly improvements of the segmentation performance of the model comparing to other similar methods, as the extraction on context information is more effectively and richer.