In:
Medical Physics, Wiley, Vol. 44, No. 5 ( 2017-05), p. 1678-1691
Abstract:
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 ( CNN s) to develop and evaluate a fully automatic detection of thyroid nodules from 2D ultrasound images. Methods Our cascade CNN s are a type of hybrid model, consisting of two different CNN s 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 CNN s 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 CNN s compared to that of other methods. The multi‐view strategy can improve the performance of cascade CNN s. 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.
Type of Medium:
Online Resource
ISSN:
0094-2405
,
2473-4209
DOI:
10.1002/mp.2017.44.issue-5
Language:
English
Publisher:
Wiley
Publication Date:
2017
detail.hit.zdb_id:
1466421-5
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