Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks
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Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images.
Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset.
The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as \(0.8683 \pm 0.0056\), \(0.9224 \pm 0.0027\), \(0.915 \pm 0.0077\), \(0.0669 \pm 0.0032\), \(0.6228 \pm 0.1414\) on overall folds, respectively.
Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.
KeywordsThyroid nodule Ultrasound image Convolutional neural network Segmentation
This work was supported in part by the National 372 Natural Science Foundation of China (Grant No. 91630311), the Fun-373 damental Research Funds for the Central Universities (Grant No. 374 2017XZZX007-02). The authors would like to thank Dr. Deepika Koundal, University Institute of Engineering and Technology, Panjab University, Chandigarh, India, for kindly providing their code.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study
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