Abstract
Unlike daily routine images, ultrasound images are usually monochrome and low-resolution. In ultrasound images, the cancer regions are usually blurred, vague margin and irregular in shape. Moreover, the features of cancer region are very similar to normal or benign tissues. Therefore, training ultrasound images with original Convolutional Neural Network (CNN) directly is not satisfactory. In our study, inspired by state-of-the-art object detection network Faster R-CNN, we develop a detector which is more suitable for thyroid papillary carcinoma detection in ultrasound images. In order to improve the accuracy of the detection, we add a spatial constrained layer to CNN so that the detector can extract the features of surrounding region in which the cancer regions are residing. In addition, by concatenating the shallow and deep layers of the CNN, the detector can detect blurrier or smaller cancer regions. The experiments demonstrate that the potential of this new methodology can reduce the workload for pathologists and increase the objectivity of diagnoses. We find that 93:5% of papillary thyroid carcinoma regions could be detected automatically while 81:5% of benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention.
Highlights
Papillary thyroid carcinoma is most common in thyroid carcinoma, accounting for 85%1
We analyze the shortcomings of the state-of-the-art object detection network Faster R-Convolutional Neural Network (CNN) for detecting ultrasound image in detail (See Section 0.7)
Experimental results show that each strategy can improve the functioning of the detection
Summary
Papillary thyroid carcinoma is most common in thyroid carcinoma, accounting for 85%1. Et al.[15] presented a new multiple field of view classifier, with different size of multiple field of view to identify the important features of one image This method was used for classification of breast cancer pathological images. Lutjanus et al.[18] used CNN to idiomatically identify the features of Sentinel and breast cancer metastasis in the MR image This method can reduce the workload of the pathologist and increase the objectivity of the diagnosis. Kersten and Chernoff et al.[20] proposed a combination of supervised learning and unsupervised learning approach to segment breast density separation and evaluate risk assessment of breast They utilized the deep unsupervised CNN to extract feature of images. We name this approach CS Faster R-CNN for short
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