Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network
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Nodule Localization in Thyroid Ultrasound Images with a Joint-Training Convolutional Neural Network Ruoyun Liu 1,2 & Shichong Zhou 3,4 & Yi Guo 1,2
&
Yuanyuan Wang 1,2 & Cai Chang 3,4
# Society for Imaging Informatics in Medicine 2020
Abstract The accurate localization of nodules in ultrasound images can convey crucial information to support a reliable diagnosis. However, this is usually challenging due to low contrast and image artifacts, especially in thyroid ultrasound images where nodules are relatively small in most cases. To address these problems, in this paper, we propose a joint-training convolutional neural network (CNN) for thyroid nodule localization in ultrasound images. Considering the advantage of the faster region-based CNN (Faster R-CNN) in detecting natural targets, we adopt it as the basic framework. To boost the representative power and noise suppression capability of the network, the attention mechanism module is embedded for adaptive feature refinement along the channel and spatial dimensions. Furthermore, in the training process, we annotate the training set in a novel way, called jointtraining annotation, by exploiting the fake foreground (FFG) area around the nodule as a spatial prior constraint to improve the sensitivity to small nodules. Ablation experiments are conducted to verify the effectiveness of our proposed method. The experimental results show that our method outperforms others by a mean average precision (mAP) of 0.93 and achieves an intersection over union (IoU) of 0.9, indicating that the localization results agree well with the ground truth. Furthermore, extended experiments on breast nodule datasets are also conducted to verify the generalizability of the proposed approach. Above all, the proposed algorithm is of considerable significance for accurate thyroid nodule localization in ultrasound images and can be generalized to other types of nodules, thereby providing trustworthy assistance for clinical diagnosis. Keywords Thyroid nodule localization . Ultrasound images . Attention mechanism module . Joint-training annotation . Fake foreground
Introduction
Ruoyun Liu and Shichong Zhou contributed equally to this work. * Yi Guo [email protected] * Yuanyuan Wang [email protected] 1
Department of Electronic Engineering, Fudan University, No. 220, Handan Road, Yangpu District, Shanghai 200433, China
2
Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Shanghai 200032, China
3
Department of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China
4
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
Thyroid cancers are nodules of the thyroid gland and originate from follicular or parafollicular cells. In most cases, a definitive etiology for thyroid cancer is not known, but radiation exposure (especially to children) and some hereditary conditions are associated with an increased risk for developing thyroid cancer. Early diagnosis of thyroid cancer contributes to the proce
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