title: Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images.
abstract: OBJECTIVE
  It is very important for calculation of clinical indices and diagnosis to detect thyroid nodules from ultrasound images. However, this task is a challenge mainly due to heterogeneous thyroid nodules with distinct components are similar to background in ultrasound images. In this study, we employ cascade deep convolutional neural networks (CNNs) to develop and evaluate a fully automatic detection of thyroid nodules from 2D ultrasound images.
  
  
  METHODS
  Our cascade CNNs are a type of hybrid model, consisting of two different CNNs and a new splitting method. Specifically, it employs a deep CNN to learn the segmentation probability maps from the ground true data. Then, all the segmentation probability maps are split into different connected regions by the splitting method. Finally, another deep CNN is used to automatically detect the thyroid nodules from ultrasound thyroid images.
  
  
  RESULTS
  Experiment results illustrate the cascade CNNs are very effective in detection of thyroid nodules. Specially, the value of area under the curve of receiver operating characteristic is 98.51%. The Free-response receiver operating characteristic (FROC) and jackknife alternative FROC (JAFROC) analyses show a significant improvement in the performance of our cascade CNNs compared to that of other methods. The multi-view strategy can improve the performance of cascade CNNs. Moreover, our special splitting method can effectively separate different connected regions so that the second CNN can correctively gain the positive and negative samples according to the automatic labels.
  
  
  CONCLUSIONS
  The experiment results demonstrate the potential clinical applications of this proposed method. This technique can offer physicians an objective second opinion, and reduce their heavy workload so as to avoid misdiagnosis causes because of excessive fatigue. In addition, it is easy and reproducible for a person without medical expertise to diagnose thyroid nodules.
authors.affiliation: School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.
authors.forename: Jinlian
authors.initials: J
authors.lastname: Ma
authors.affiliation: School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.
authors.forename: Fa
authors.initials: F
authors.lastname: Wu
authors.affiliation: Department of Ultrasound, First Affiliated Hospital, Zhejiang University, Hangzhou, 310003, China.
authors.forename: Tian'an
authors.initials: T
authors.lastname: Jiang
authors.affiliation: Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310020, China.
authors.forename: Jiang
authors.initials: J
authors.lastname: Zhu
authors.affiliation: School of Mathematical Sciences, Zhejiang University, Hangzhou, 310027, China.
authors.forename: Dexing
authors.initials: D
authors.lastname: Kong
mesh_terms: D006801:Humans; D016571:Neural Networks, Computer; D011336:Probability; D012372:ROC Curve; D016606:Thyroid Nodule / Q000000981:diagnostic imaging*; D014463:Ultrasonography / Q000379:methods*
keywords: convolutional neural network; detection; feature extraction; segmentation; thyroid nodule; ultrasound image