COMA-Net: Towards generalized medical image segmentation using complementary attention guided bipolar refinement modules

Precise medical image segmentation is a crucial step for proper isolation of target regions, such as an organ or lesion for accurate medical diagnosis, prognosis and certain medical procedures. Taking advantage of the available annotated medical image datasets, many CNN-based approaches have been proposed for segmentation over the years. These conventional approaches lack appropriate supervised means to enhance the foreground/target regions relative to background at the feature level for improving the generalizability of these methods to obtain better performance across diverse imaging modalities. In this work, we introduce COMA-Net (COMplementary Attention guided bipolar refinement-based Network), which employs a complementary attention scheme between a pair of positive and negative refinement modules placed on top of two encoder structures for generating refined feature references for the decoder stage in a supervised manner. A four-way feature shifting operation is introduced in conjunction with a set of dilated convolutional layers so that it considers the spatial relationships across a wider footprint leading to better contextual feature extraction. We also formulate a novel Foreground-to-Background Ratio (FBR) index to highlight the differences in signal power between target region and background due to the refinement. Experimental results on five different publicly available medical image segmentation datasets, including BUSI, GLAS, ISIC-2018, MoNuSeg and CVC-ClinicDB reveal that on average, the proposed method can achieve an additional mean F1, IoU, precision, and recall score of +0.97%, +1.25%, +1.11%, and +0.22%, respectively over the state-of-the-art segmentation methods, suggesting its great potential for application on real-world patient image data.