Multi-Level Object-Aware Guidance Network for Biomedical Image Segmentation

Abstract

Most state-of-the-art models for biomedical image segmentation are developed based on U-shape architecture, which has two renowned, yet mutually affected, shortcomings: 1) difficulties in capturing global long-range dependencies, and 2) semantic information dilution in the decoding process. In this paper, we propose a novel network with a new object-aware module (OAM) to effectively establish global dependencies at multiple levels within the network and compensate high-level semantic information dilution when fusing the extracted multi-level features; we call the network MOG-Net. Specifically, the OAM is designed to figure out the relations between each pixel and targeting object region and recalibrate class-level semantic information according to the relations. Compared with non-local models, which construct pixel-wise global dependencies, our OAM is more efficient and target-specific, enabling us to achieve satisfactory results with less extra computational overhead. In addition, we embed a pyramid context encoder module (PCEM) in the proposed OAM to alleviate semantic information dilution; this scheme is able to bridge the spatial-semantic gap when fusing features extracted from different levels. We extensively evaluate the proposed MOG-Net on four diverse biomedical image segmentation tasks with different imaging modalities, achieving segmentation performance with 88.19%, 90.95% and 66.03% in Dice on three one-class datasets, as well as 88.83% and 87.11% in Dice for two classes on a multi-class dataset, respectively. Experimental results demonstrate the effectiveness of the proposed method, consistently outperforming state-of-the-art methods in most evaluation metrics. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Semantic segmentation of biomedical images is a critical prerequisite for subsequent diagnosis, treatment, and quantitative tasks in clinical practice. This article proposes a novel biomedical image segmentation network, namely MOG-Net, with a new object-aware module (OAM) to model global context dependencies from a category perspective and a pyramid context encoder module (PCEM) to enhance feature representation capabilities of spatial and channel dimensions. We experimentally demonstrate the effectiveness and generalization capability of proposed MOG-Net on diverse biomedical image segmentation tasks with different imaging modalities. We believe that our proposed method can serve as a practical clinical tool and has the potential to be applied to existing computer-aided medical systems and clinical measurement.