NFE-Net: Detection and Segmentation of Thyroid Nodules in Ultrasound Images Based on Nodule Feature Enhanced

Abstract

Deep learning-based methods are commonly used for thyroid nodules detection and segmentation in ultrasound images, but the shape and size of the nodules vary greatly, there are also solid nodules that closely resemble the background and device-induced artifacts, making it difficult to accurately localize and segment the nodules. In this paper, NFE-Net is proposed to address the above difficulties, which designs receptive field enhancement path (RFEP) and texture-boundary guidance path (TBGP) in the neck of the Mask RCNN for nodule feature enhancement, so as to improve the localization and segmentation performance of network. RFEP enhances the learning ability of the network for nodule’s scale by introducing multi-scale receptive field enhancement module (RFEM); TBGP computes the channel correlation to extract the texture and boundary features of the nodule in information extraction module (IEM), further, uses true texture and boundary mask for deep supervised learning, finally fuses the upper and lower layers of the features by feature fusion block (FFB). Experimental results on the public thyroid datasets TN3K, DDTI show that our approach outperforms six state-of-the-art methods.

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