Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extractio
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(2020) 20:215
RESEARCH ARTICLE
Open Access
Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extraction Hong Zhu1,2* , Qianhao Fang1, Yihe Huang1 and Kai Xu3*
Abstract Background: Accurately determining the softness level of pituitary tumors preoperatively by using their image textures can provide a basis for surgical options and prognosis. Existing methods for this problem require manual intervention, which could hinder the efficiency and accuracy considerably. Methods: We present an automatic method for diagnosing the texture of pituitary tumors using unbalanced sequence image data. Firstly, for the small sample problem in our pituitary tumor MRI image dataset where T1 and T2 sequence data are unbalanced (due to data missing) and under-sampled, our method uses a CycleGAN (CycleConsistent Adversarial Networks) model for domain conversion to obtain fully sampled MRI spatial sequence. Then, it uses a DenseNet (Densely Connected Convolutional Networks)-ResNet(Deep Residual Networks) based Autoencoder framework to optimize the feature extraction process for pituitary tumor image data. Finally, to take advantage of sequence data, it uses a CRNN (Convolutional Recurrent Neural Network) model to classify pituitary tumors based on their predicted softness levels. Results: Experiments show that our method is the best in terms of efficiency and accuracy (91.78%) compared to other methods. Conclusions: We propose a semi-supervised method for grading pituitary tumor texture. This method can accurately determine the softness level of pituitary tumors, which provides convenience for surgical selection and prognosis, and improves the diagnostic efficiency of pituitary tumors. Keywords: Pituitary tumors, CycleGAN, DenseNet, ResNet, Auto-encoder, CRNN
Background Pituitary tumor is one of the most common diseases in the nervous system [1]. It is the third largest tumor type in brain and is extremely harmful to the human body [2]. Many critical questions, such as whether a surgical procedure is needed, what kind of procedure is most suitable, and what is the expected postoperative effect, are all closely related to the softness of pituitary tumor * Correspondence: [email protected]; [email protected] 1 School of Medical Information, Xuzhou Medical University, Xuzhou, China 3 Affiliated Hospital of Xuzhou Medical University, Xuzhou, China Full list of author information is available at the end of the article
[3]. It is important to accurately judge the softness level of pituitary tumor preoperatively in a non-invasive manner. This has been a problem for a long time and is still plaguing the clinic. However, due to the closure nature of the cranial cavity, it is often difficult to accurately determine the softness of pituitary tumor before surgery [4]. With the technological advancements in medical imaging, MR, CT and other imaging modality can now provide rich anatomical information non-invasively. It has been shown that such information can be used to improve the
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