Accurate segmentation of the adrenal gland from abdominal computed tomography (CT) scans is a crucial step towards facilitating the computer-aided diagnosis of adrenal-related diseases such as essential hypertension and adrenal tumors. However, the small size of the adrenal gland, which occupies less than 1% of the abdominal CT slice, poses a significant challenge to accurate segmentation. To address this problem, we propose a novel multi-level context-aware network (MCNet) to segment adrenal glands in CT images. Our MCNet mainly consists of two components, i.e., the multi-level context aggregation (MCA) module and multi-level context guidance (MCG) module. Specifically, the MCA module employs multi-branch dilated convolutional layers to capture geometric information, which enables handling of changes in complex scenarios such as variations in the size and shape of objects. The MCG module, on the other hand, gathers valuable features from the shallow layer and leverages the complete utilization of feature information at different resolutions in various codec stages. Finally, we evaluate the performance of the MCNet on two CT datasets, including our clinical dataset (Ad-Seg) and a publicly available dataset known as Distorted Golden Standards (DGS), from different perspectives. Compared to ten other state-of-the-art segmentation methods, our MCNet achieves 71.34% and 75.29% of the best Dice similarity coefficient on the two datasets, respectively, which is at least 2.46% and 1.19% higher than other segmentation methods.
Keywords: Adrenal gland; CT; Image segmentation; Multi-level context aggregation; Multi-level context guidance.
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