A novel improved deep convolutional neural network model for medical image fusion

Xia, Kaijian; Yin, H.; Jiang-qiang, Wang

doi:10.1007/s10586-018-2026-1

Cited by 93 publications

(31 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They demonstrated that the network could rapidly and accurately quantify registration performance. Multi-grid Inference [163] Brain 3D-3D MR-US Rigid LSTM [8] Brain 2D-2D CT, T1, T2, PD Rigid Manifold Learning [165] Brain, Abdomen 2D-3D CT-PET, CT-MRI Deformable CAE, DSCNN [178] Spine 3D-2D 3DCT-Xray Rigid FasterRCNN [54] Brain 2D-2D T1-T2, T1-PD Deformable FCN [183] Spine 3D-2D 3DCT-Xray Rigid Domain adaptation…”

Section: Registration Validation Using Deep Learningmentioning

confidence: 99%

Deep learning in medical image registration: a review

et al. 2020

View full text Add to dashboard Cite

This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration. 1 arXiv:1912.12318v1 [eess.IV] 27 Dec 2019 1. Summarize the latest developments in DL-based medical image registration. 2. Highlight contributions, identify challenges and outline future trends. 3. Provide detailed statistics on recent publications from different perspectives. 2 Deep Learning 2 Convolutional Neural NetworkConvolutional neural network (CNN) is a class of deep neural networks with regularized multilayer perceptron. CNN uses convolution operation in place of general matrix multiplication in simple neural networks. The convolutional filters and operations in CNN make it suitable for visual imagery signal processing. Because of its excellent feature extraction ability, CNN is one of the most successful models for image analysis. Since the breakthrough of AlexNet [79], many variants of CNN have been proposed and have achieved the-state-of-art performances in various image processing tasks. A typical CNN usually consists of multiple convolutional layers, max pooling layers, batch normalization layers, dropout layers, a sigmoid or softmax layer. In each convolutional layer, multiple channels of feature maps were extracted by sliding trainable convolutional kernels across the input feature maps. Hierarchical features with high-level abstraction are extracted using multiple convolutional layers. These feature maps usually go through multiple fully connected layer before reaching the final decision layer. Max pooling layers are often used to reduce the image sizes and to promote spatial invariance of the network. Batch normalization is used to reduce internal covariate shift among the training samples. Weight regularization and dropout layers are used to alleviate data overfitting. The loss function is defined as the difference between the predicted and the target output. CNN is usually trained by minimizing the loss via gradient back propagation using optimization methods. Many different types of network architectures have been proposed to improve the performance of CNN [93]. U-Net proposed by Ronneberger et al. is among one of the most used network architectures [120]. U-Net was originally used to perform neuronal structures segmentation. U-Net adopts symmetrical contractiv...

show abstract

Section: Registration Validation Using Deep Learningmentioning

confidence: 99%

Deep learning in medical image registration: a review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…At present, CNN plays an increasingly important role in medical image fusion. Xia integrated multi-scale transform and CNN into a multi-modality medical image fusion framework, which uses the deep stacked neural network to divide source images into high-and low-frequency components to do corresponding image fusion [37]. Liu proposed a CNN-based multi-modality medical image fusion algorithm, which applies image pyramids to the medical image fusion process in a multi-scale manner [38].…”

Section: Related Workmentioning

confidence: 99%

Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid

Wang

Zheng

Wei

et al. 2020

Sensors

View full text Add to dashboard Cite

Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high visual quality and clear structure details, this paper proposes a convolutional neural network (CNN) based medical image fusion algorithm. The proposed algorithm uses the trained Siamese convolutional network to fuse the pixel activity information of source images to realize the generation of weight map. Meanwhile, a contrast pyramid is implemented to decompose the source image. According to different spatial frequency bands and a weighted fusion operator, source images are integrated. The results of comparative experiments show that the proposed fusion algorithm can effectively preserve the detailed structure information of source images and achieve good human visual effects.

show abstract

“…A method using superpixel segmentation and naive Bayes classifier for bleeding frames detection was recently proposed in [43]. In [23,41,51], systems for small intestine motility characterization and bleeding detection, based on deep convolutional neural networks were introduced. The CE scores to assess small-bowel inflammatory activity in Crohn's disease were evaluated in [37].…”

Section: Related Workmentioning

confidence: 99%

Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images

Charfi

Ansari

Balasingham

2019

IET Image Processing

View full text Add to dashboard Cite

Wireless capsule endoscopy (WCE) has revolutionized the diagnosis and treatment of gastrointestinal tract, especially the small intestine which is unreachable by traditional endoscopies. The drawback of the WCE is that it produces a large amount of images to be inspected by the clinicians. Hence, the design of a computer-aided diagnosis (CAD) system will have a great potential to help reducing the diagnosis time and improve the detection accuracy. To address this problem, we propose a CAD system for automatic detection of ulcer in WCE images. Firstly, we enhance the input images to be better exploited in the main steps of the proposed method. Afterwards, segmentation using saliency map based texture and colour is applied to the WCE images in order to highlight ulcerous regions. Then, inspired by the existing feature extraction approaches, a new one has been proposed for the recognition of the segmented regions. Finally, a new recognition scheme is proposed based on hidden markov model using the classification scores of the conventional methods (support vector machine, multilayer perceptron and random forest) as observations. Experimental results with two different datasets show that the proposed method gives promising results.

show abstract

A novel improved deep convolutional neural network model for medical image fusion

Cited by 93 publications

References 21 publications

Deep learning in medical image registration: a review

Deep learning in medical image registration: a review

Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid

Computer‐aided diagnosis system for ulcer detection in wireless capsule endoscopy images

Contact Info

Product

Resources

About