Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images

المؤلفون المشاركون

Xiong, Jing
Li, Wen
Li, Yafen
Xie, Yaoqin
Xia, Jun

المصدر

BioMed Research International

العدد

المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-9، 9ص.

الناشر

Hindawi Publishing Corporation

تاريخ النشر

2020-11-05

دولة النشر

مصر

عدد الصفحات

9

التخصصات الرئيسية

الطب البشري

الملخص EN

Cross-modality medical image synthesis between magnetic resonance (MR) images and computed tomography (CT) images has attracted increasing attention in many medical imaging area.

Many deep learning methods have been used to generate pseudo-MR/CT images from counterpart modality images.

In this study, we used U-Net and Cycle-Consistent Adversarial Networks (CycleGAN), which were typical networks of supervised and unsupervised deep learning methods, respectively, to transform MR/CT images to their counterpart modality.

Experimental results show that synthetic images predicted by the proposed U-Net method got lower mean absolute error (MAE), higher structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) in both directions of CT/MR synthesis, especially in synthetic CT image generation.

Though synthetic images by the U-Net method has less contrast information than those by the CycleGAN method, the pixel value profile tendency of the synthetic images by the U-Net method is closer to the ground truth images.

This work demonstrated that supervised deep learning method outperforms unsupervised deep learning method in accuracy for medical tasks of MR/CT synthesis.

نمط استشهاد جمعية علماء النفس الأمريكية (APA)

Li, Yafen& Li, Wen& Xiong, Jing& Xia, Jun& Xie, Yaoqin. 2020. Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images. BioMed Research International،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1134563

نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)

Li, Yafen…[et al.]. Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images. BioMed Research International No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1134563

نمط استشهاد الجمعية الطبية الأمريكية (AMA)

Li, Yafen& Li, Wen& Xiong, Jing& Xia, Jun& Xie, Yaoqin. Comparison of Supervised and Unsupervised Deep Learning Methods for Medical Image Synthesis between Computed Tomography and Magnetic Resonance Images. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1134563

نوع البيانات

مقالات

لغة النص

الإنجليزية

الملاحظات

Includes bibliographical references

رقم السجل

BIM-1134563