Feature Pyramid Network With Level-Aware Attention for Meningioma Segmentation

Abstract

Meningiomas are the most common primary intracranial tumors in adults, and they are dangerous and even lethal when they grow and oppress vital organs. In clinical, microsurgical resection is the most widely used treatment for most meningiomas. And tumor segmentation is an essential primary step before applying any therapy. However, due to the various meningioma locations and complicated intracranial structures, it is still challenging to segment the tumor accurately in both boundaries and contour automatically. In this work, a novel method is proposed for the automatic segmentation of meningioma. In general, the proposed method follows a coarse-to-fine strategy. Specifically, the feature pyramid structure is employed to extract multi-level features. Further, a Level-aware Attention is proposed to refine multi-level features by naturally utilizing the complementary features of different levels, thus significantly improving the segmentation performance. Moreover, to validate the proposed method, a realistic dataset of meningioma with fine labels is constructed, and experimental results on the dataset demonstrate the effectiveness of the proposed method.