Abstract
BackgroundThere are many types of hand tumors, and it is often difficult for imaging diagnosticians to make a correct diagnosis, which can easily lead to misdiagnosis and delay in treatment. Thus in this paper, we propose a deep neural network for diagnose on MR Images of tumors of the hand in order to better define preoperative diagnosis and standardize surgical treatment.MethodsWe collected MRI figures of 221 patients with hand tumors from one medical center from 2016 to 2019, invited medical experts to annotate the images to form the annotation data set. Then the original image is preprocessed to get the image data set. The data set is randomly divided into ten parts, nine for training and one for test. Next, the data set is input into the neural network system for testing. Finally, average the results of ten experiments as an estimate of the accuracy of the algorithm.ResultsThis research uses 221 images as dataset and the system shows an average confidence level of 71.6% in segmentation of hand tumors. The segmented tumor regions are validated through ground truth analysis and manual analysis by a radiologist.ConclusionsWith the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder decoder deep architectures. Therefore, in this paper, we propose an automatic segmentation method based on DeepLab v3+ and achieved a good diagnostic accuracy rate.
Highlights
In medical treatment, a radiograph of the hand is mandatory for all lesions
With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder decoder deep architectures
In this paper, we propose an automatic segmentation method based on DeepLab v3+ and achieved a good diagnostic accuracy rate
Summary
A radiograph of the hand is mandatory for all lesions. Several major types of hand tumor are benign, and malignant tumors are appeared in specific area with its specific character. 69.2% -95% of tumors of the hand do not involve cutaneous malignancy are benign [1, 2]. The patient’s history, physical examination, radiography and laboratory examinations are essential factors for diagnosis of hand tumors. There are many types of hand tumors, and it is often difficult for imaging diagnosticians to make a correct diagnosis, which can lead to misdiagnosis and delay in treatment. We propose a deep neural network for diagnose on MR Images of tumors of the hand in order to better define preoperative diagnosis and standardize surgical treatment
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