Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model

R, Hou; Zhou, Dongming; Nie, Rencan; Dong, Liyun; Ruan, Xiaoli

doi:10.1007/s11517-018-1935-8

Cited by 81 publications

(45 citation statements)

References 24 publications

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“…The preprocessing SAE was added to the CNN classifier, which is better than the previous CNN. The fusion method based on CNN began to develop [62][63][64].…”

Section: Image Fusion Based On Deepmentioning

confidence: 99%

A Review of Multimodal Medical Image Fusion Techniques

Huang

Yang

Yin

et al. 2020

Computational and Mathematical Methods in Medicine

120

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The medical image fusion is the process of coalescing multiple images from multiple imaging modalities to obtain a fused image with a large amount of information for increasing the clinical applicability of medical images. In this paper, we attempt to give an overview of multimodal medical image fusion methods, putting emphasis on the most recent advances in the domain based on (1) the current fusion methods, including based on deep learning, (2) imaging modalities of medical image fusion, and (3) performance analysis of medical image fusion on mainly data set. Finally, the conclusion of this paper is that the current multimodal medical image fusion research results are more significant and the development trend is on the rise but with many challenges in the research field.

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“…The preprocessing SAE was added to the CNN classifier, which is better than the previous CNN. The fusion method based on CNN began to develop [62][63][64].…”

Section: Image Fusion Based On Deepmentioning

confidence: 99%

A Review of Multimodal Medical Image Fusion Techniques

Huang

Yang

Yin

et al. 2020

Computational and Mathematical Methods in Medicine

120

View full text Add to dashboard Cite

show abstract

“…Kumar et al [147] developed a supervised CNN to learn to merge the data from PET-CT images of lung cancer. CNN has also been applied to fuse medical images MRI/CT, MRI/SPECT, multiparametric MR images [148] and PET/MRI [149]. CNN can also be combined with a wavelet transform for the fusion of CT and MR images [150].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

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“…Many neuroscientists have studied the structural and functional relationships between the brain and brain potential mapping to understand and treat neurological diseases (Woo et al, 2017;Lake et al, 2018;Murphy et al, 2019). Although, there are many available imaging techniques for brain mapping, such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and electroencephalography (EEG), electrical activity in the brain is difficult to monitor using current techniques due to slow response or low resolution (Damborská et al, 2019;Glaab et al, 2019;Hou et al, 2019;Huhn et al, 2019;Klauser et al, 2019). Numerous electronics, such as cortical probes and electrodes, which use penetrating probes or surface electrodes, have been developed with various materials to reliably acquire an accurate electrical signal.…”

Section: In Vivo Monitoring Of Electrophysiological Signalsmentioning

confidence: 99%

Flexible Electronics for Monitoring in vivo Electrophysiology and Metabolite Signals

Choi

Lee

et al. 2020

Front. Chem.

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Numerous efforts have been made to develop efficient biosensors for detecting analytes in the human body. However, biosensors are often developed on rigid materials, which limits their application on skin, organs, and other tissues in the human body where good flexibility is required. Developing flexible materials for biosensors that can be used on soft and irregularly shaped surfaces would significantly expand the clinical application of biosensors. In this review, we will provide a selective overview of recently developed flexible electronic devices and their applications for monitoring in vivo metabolite and electrophysiology signals. The article provides guidelines for the development of an in vivo signal monitoring system and emphasizes research from various disciplines for the further development of flexible electronics that can be used in more biomedical applications in the future.

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Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model

Cited by 81 publications

References 24 publications

A Review of Multimodal Medical Image Fusion Techniques

A Review of Multimodal Medical Image Fusion Techniques

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Flexible Electronics for Monitoring in vivo Electrophysiology and Metabolite Signals

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