Current medical imaging · 2020
An Automatic Classification of the Early Osteonecrosis of Femoral Head with Deep Learning.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Zhu Liyang, Han Jungang, Guo Renwen, Wu Dong, Wei Qiang, Chai Wei, Tang Shaojie
Аннотация
BACKGROUND: Osteonecrosis of Femoral Head (ONFH) is a common complication in orthopaedics, wherein femoral structures are usually damaged due to the impairment or interruption of femoral head blood supply.
AIM: In this study, an automatic approach for the classification of the early ONFH with deep learning has been proposed.
METHODS: All femoral CT slices according to their spatial locations with the Convolutional Neural Network (CNN) are first classified. Therefore, all CT slices are divided into upper, middle or lower segments of femur head. Then the femur head areas can be segmented with the Conditional Generative Adversarial Network (CGAN) for each part. The Convolutional Autoencoder is employed to reduce dimensions and extract features of femur head, and finally K-means clustering is used for an unsupervised classification of the early ONFH.
RESULTS: To invalidate the effectiveness of the proposed approach, the experiments on the dataset with 120 patients are carried out. The experimental results show that the segmentation accuracy is higher than 95%. The Convolutional Autoencoder can reduce the dimension of data, the Peak Signal- to-Noise Ratios (PSNRs) are better than 34dB for inputs and outputs. Meanwhile, there is a great intra-category similarity, and a significant inter-category difference.
CONCLUSION: The research on the classification of the early ONFH has a valuable clinical merit, and hopefully it can assist physicians to apply more individualized treatment for patient.
AIM: In this study, an automatic approach for the classification of the early ONFH with deep learning has been proposed.
METHODS: All femoral CT slices according to their spatial locations with the Convolutional Neural Network (CNN) are first classified. Therefore, all CT slices are divided into upper, middle or lower segments of femur head. Then the femur head areas can be segmented with the Conditional Generative Adversarial Network (CGAN) for each part. The Convolutional Autoencoder is employed to reduce dimensions and extract features of femur head, and finally K-means clustering is used for an unsupervised classification of the early ONFH.
RESULTS: To invalidate the effectiveness of the proposed approach, the experiments on the dataset with 120 patients are carried out. The experimental results show that the segmentation accuracy is higher than 95%. The Convolutional Autoencoder can reduce the dimension of data, the Peak Signal- to-Noise Ratios (PSNRs) are better than 34dB for inputs and outputs. Meanwhile, there is a great intra-category similarity, and a significant inter-category difference.
CONCLUSION: The research on the classification of the early ONFH has a valuable clinical merit, and hopefully it can assist physicians to apply more individualized treatment for patient.