Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extraction

Zhu, Hong; FANG, Qianhao; HUANG, Yihe; Xu, Kai

doi:10.1186/s12911-020-01230-x

Cited by 15 publications

(8 citation statements)

References 15 publications

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“…To our knowledge, no previous study has applied unsupervised DL to a small dataset of CT images. Most DL studies involving small numbers of medical images used supervised [22][23][24] , or semi-supervised DL [25][26][27] . These approaches may be used because supervised DL algorithms are expected to identify features that distinguish among data in small datasets; they require definite answers to focus on during the model training.…”

Section: Discussionmentioning

confidence: 99%

Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder

Chung

Choi

2023

Sci Rep

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We sought to establish an unsupervised algorithm with a three–dimensional (3D) variational autoencoder model (VAE) for the detection of abnormal extraocular muscles in small datasets of orbital computed tomography (CT) images. 334 CT images of normal orbits and 96 of abnormal orbits diagnosed as thyroid eye disease were used for training and validation; 24 normal and 11 abnormal orbits were used for the test. A 3D VAE was developed and trained. All images were preprocessed to emphasize extraocular muscles and to suppress background noise (e.g., high signal intensity from bones). The optimal cut-off value was identified through receiver operating characteristic (ROC) curve analysis. The ability of the model to detect muscles of abnormal size was assessed by visualization. The model achieved a sensitivity of 79.2%, specificity of 72.7%, accuracy of 77.1%, F1-score of 0.667, and AUROC of 0.801. Abnormal CT images correctly identified by the model showed differences in the reconstruction of extraocular muscles. The proposed model showed potential to detect abnormalities in extraocular muscles using a small dataset, similar to the diagnostic approach used by physicians. Unsupervised learning could serve as an alternative detection method for medical imaging studies in which annotation is difficult or impossible to perform.

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Section: Discussionmentioning

confidence: 99%

Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder

Chung

Choi

2023

Sci Rep

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“…Zhu and colleagues ( 12 ) also used 152 patient data with labels (including 112 T1 MRI spatial sequences and 40 T2 MRI spatial sequences) and presented an automatic method for accurately determining the softness level of pituitary tumors preoperatively. Because their pituitary tumor MRI image dataset where T1 and T2 sequence data are unbalanced (due to data missing) and undersampled.…”

Section: Magnetic Resonance Imaging-based Radiomics and ML In Pasmentioning

confidence: 99%

“…More ML and feature selection algorithms and more comprehensive MRI data including contrast-enhanced MRI scans may have a potential for developing better ML-based models. Thirdly, the data samples used in Zhu’s ( 12 ) study were unbalanced sequence image data and insufficient; it is easy to produce the overfitting phenomenon. Although the loss of feature extraction model training was low and convergence was achieved, the accuracy was still not high enough.…”

Section: Magnetic Resonance Imaging-based Radiomics and ML In Pasmentioning

confidence: 99%

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

et al. 2021

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Pituitary adenomas (PAs) are a group of tumors with complex and heterogeneous clinical manifestations. Early accurate diagnosis, individualized management, and precise prediction of the treatment response and prognosis of patients with PA are urgently needed. Artificial intelligence (AI) and machine learning (ML) have garnered increasing attention to quantitatively analyze complex medical data to improve individualized care for patients with PAs. Therefore, we critically examined the current use of AI and ML in the management of patients with PAs, and we propose improvements for future uses of AI and ML in patients with PAs. AI and ML can automatically extract many quantitative features based on massive medical data; moreover, related diagnosis and prediction models can be developed through quantitative analysis. Previous studies have suggested that AI and ML have wide applications in early accurate diagnosis; individualized treatment; predicting the response to treatments, including surgery, medications, and radiotherapy; and predicting the outcomes of patients with PAs. In addition, facial imaging-based AI and ML, pathological picture-based AI and ML, and surgical microscopic video-based AI and ML have also been reported to be useful in assisting the management of patients with PAs. In conclusion, the current use of AI and ML models has the potential to assist doctors and patients in making crucial surgical decisions by providing an accurate diagnosis, response to treatment, and prognosis of PAs. These AI and ML models can improve the quality and safety of medical services for patients with PAs and reduce the complication rates of neurosurgery. Further work is needed to obtain more reliable algorithms with high accuracy, sensitivity, and specificity for the management of PA patients.

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“…However, due to the nature of cranial cavity, it is often difficult to determine the softness of pituitary tumors before surgery. Therefore, how to accurately determine the softness of pituitary tumors in a noninvasive manner has become an important issue [ 2 ]. With the advancement of medical imaging technology, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and other imaging methods have become an important basis for assisting doctors in diagnosis.…”

Section: Introductionmentioning

confidence: 99%

MR Image Classification for Brain Tumor Texture Based on Pseudo-Label Learning and Optimized Feature Extraction

Liu

et al. 2022

Computational and Mathematical Methods in Medicine

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Brain tumors are the deadliest and most difficult to treat of all forms of cancer. Preoperative classification of brain tumors is conducive to the development of corresponding treatment plan. Take pituitary tumors as an example. Precisely judging the image data of pituitary tumor texture before surgery can provide a basis for the selection of surgical plan and prognosis. However, the existing methods require manual intervention, and the efficiency and accuracy are not high. In this paper, we proposed an automatic brain tumor texture diagnosis method for uneven sequence image data. First, for the small sample of pituitary tumor MRI image data, the T1 and T2 sequence data are uneven or missing; we used the CycleGAN model to perform data conversion between different domains to obtain a completely sampled MRI spatial sequence. Then, we used texture analysis+pseudo-label learning to label pituitary tumor data of some unknown labels. After that, we used the improved U-Net model based on CBAM to optimize feature extraction for pituitary tumor image data. Finally, we used the CRNN model to classify the degree of pituitary tumor texture based on the advantages of sequence data. The entire process only needs to provide labels for the entire sequence data, and the efficiency is greatly improved, with an accuracy rate of 94.23%.

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Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extraction

Cited by 15 publications

References 15 publications

Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder

Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder

The Application of Artificial Intelligence and Machine Learning in Pituitary Adenomas

MR Image Classification for Brain Tumor Texture Based on Pseudo-Label Learning and Optimized Feature Extraction

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