Brain CT registration using hybrid supervised convolutional neural network

Abstract Background Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration. Method HSCN-Net generated synthetic deformation fields using a simulator as one supervision for one reference–moving image pair to address the problem of lack of gold standards. Furthermore, the simulator was designed to generate multiscale affine and elastic deformation fields to overcome the registration challenge posed by large intersubject anatomical deformation. Finally, HSCN-Net adopted a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization capability. In this work, 101 CT images of patients were collected for model construction (57), evaluation (14), and testing (30). HSCN-Net was compared with the classical Demons and VoxelMorph models. Qualitative analysis through the visual evaluation of critical brain tissues and quantitative analysis by determining the endpoint error (EPE) between the predicted sparse deformation vectors and gold-standard sparse deformation vectors, image normalized mutual information (NMI), and the Dice coefficient of the middle cerebral artery (MCA) blood supply area were carried out to assess model performance comprehensively. Results HSCN-Net and Demons had a better visual spatial matching performance than VoxelMorph, and HSCN-Net was more competent for smooth and large intersubject deformations than Demons. The mean EPE of HSCN-Net (3.29 mm) was less than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the mean Dice of HSCN-Net was 0.96, which was higher than that of Demons (0.90) and VoxelMorph (0.87); and the mean NMI of HSCN-Net (0.83) was slightly lower than that of Demons (0.84), but higher than that of VoxelMorph (0.81). Moreover, the mean registration time of HSCN-Net (17.86 s) was shorter than that of VoxelMorph (18.53 s) and Demons (147.21 s). Conclusion The proposed HSCN-Net could achieve accurate and rapid intersubject brain CT registration.

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Brain CT Registration Using Hybrid Supervised Convolutional Neural Network

10.21203/rs.3.rs-779429/v1 ◽

2021 ◽

Author(s):

Hongmei Yuan ◽

MingLei Yang ◽

Shan Qian ◽

WenXin Wang ◽

XiaoTian Jia ◽

...

Keyword(s):

Neural Network ◽

Qualitative Analysis ◽

Convolutional Neural Network ◽

Model Performance ◽

Brain Morphology ◽

Image Evaluation ◽

Registration Accuracy ◽

Visual Evaluation ◽

Normalized Mutual Information ◽

Brain Ct

Abstract Background: Brain computed tomography (CT) image registration is an essential step in the image evaluation of acute cerebrovascular disease (ACVD). Due to the complexity of human brain morphology, low brain CT soft-tissue resolution, low gray/white matter contrast, and the large anatomy variation across individuals, it is still a great challenge to perform brain CT registration accurately and rapidly. This study developed a hybrid supervised convolutional neural network (HSCN-Net) which may be used for assessment of ACVD in brain CT.Method: HSCN-Net generates synthetic deformation fields by a simulator to solve the lack of registration gold standard. The simulator are used to generate multi-scale deformation fields to overcome the registration challenge of large deformation. HSCN-Net adopts a hybrid loss function constituted by deformation field and image similarity to improve registration accuracy and generalization ability. In this work, one hundred and one brain CT images were included for HSCN-Net training and evaluation, and the results were compared with Demons and VoxelMorph. Qualitative analysis by visual evaluation, as well as quantitative analysis by Endpoint Error (EPE) between deformation fields, image Normalized Mutual Information (NMI), and Dice coefficient were carried out to access the model performance.Results: Qualitative analysis of HSCN-Net was similar to that of Demons, and both were superior to that of VoxelMorph. Moreover, HSCN-Net was more competent for large and smooth deformations. For quantitative evaluation, the EPE mean of HSCN-Net (3.29 mm) was lower than that of Demons (3.47 mm) and VoxelMorph (5.12 mm); the Dice mean of HSCN-Net was 0.96, which was better than that of Demons (0.90) and VoxelMorph (0.87); and the NMI mean of HSCN-Net (0.83) was slightly lower than that of Demons(0.84) but higher than that of VoxelMorph (0.81). In addition, the mean registration time of HSCN-Net (17.86 s) was lower than that of VoxelMorph (18.53 s) and Demons (147.21 s).Conclusion: The proposed hybrid supervised convolution registration network can achieve accurate and rapid brain CT registration. It is helpful for improving image evaluation of ACVD, thereby assisting clinicians in diagnosis and treatment decision-making.

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Deep convolutional neural network for reduction of contrast-enhanced region on CT images

Journal of Radiation Research ◽

10.1093/jrr/rrz030 ◽

2019 ◽

Vol 60 (5) ◽

pp. 586-594 ◽

Cited By ~ 1

Author(s):

Iori Sumida ◽

Taiki Magome ◽

Hideki Kitamori ◽

Indra J Das ◽

Hajime Yamaguchi ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Ct Images ◽

Deep Convolutional Neural Network ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Significant Difference ◽

Pixel Value ◽

The Mean ◽

Contrast Image

Abstract This study aims to produce non-contrast computed tomography (CT) images using a deep convolutional neural network (CNN) for imaging. Twenty-nine patients were selected. CT images were acquired without and with a contrast enhancement medium. The transverse images were divided into 64 × 64 pixels. This resulted in 14 723 patches in total for both non-contrast and contrast-enhanced CT image pairs. The proposed CNN model comprises five two-dimensional (2D) convolution layers with one shortcut path. For comparison, the U-net model, which comprises five 2D convolution layers interleaved with pooling and unpooling layers, was used. Training was performed in 24 patients and, for testing of trained models, another 5 patients were used. For quantitative evaluation, 50 regions of interest (ROIs) were selected on the reference contrast-enhanced image of the test data, and the mean pixel value of the ROIs was calculated. The mean pixel values of the ROIs at the same location on the reference non-contrast image and the predicted non-contrast image were calculated and those values were compared. Regarding the quantitative analysis, the difference in mean pixel value between the reference contrast-enhanced image and the predicted non-contrast image was significant (P < 0.0001) for both models. Significant differences in pixels (P < 0.0001) were found using the U-net model; in contrast, there was no significant difference using the proposed CNN model when comparing the reference non-contrast images and the predicted non-contrast images. Using the proposed CNN model, the contrast-enhanced region was satisfactorily reduced.

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SEGMENTATION OF HEMORRHAGE AREAS ON BRAIN CT IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

International scientific journal Internauka ◽

10.25313/2520-2057-2021-8-7398 ◽

2017 ◽

Author(s):

Vladyslav Yurochkin ◽

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Ct Images ◽

Brain Ct ◽

And Training

The paper considers the construction of a system for visualization of hemorrhage segmentation on brain CT images by creating and training a convolutional neural network to optimize the procedure for finding pathology in CT diagnostics.

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Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Computational Intelligence and Neuroscience ◽

10.1155/2019/4629859 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 13

Author(s):

Awwal Muhammad Dawud ◽

Kamil Yurtkan ◽

Huseyin Oztoprak

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Ct Images ◽

Classification Task ◽

Svm Classifier ◽

Brain Ct ◽

The Brain ◽

Brain Haemorrhage

In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolutional neural network (CNN), the well-known AlexNet neural network, and also a modified novel version of AlexNet with support vector machine (AlexNet-SVM) classifier are trained to classify the brain computer tomography (CT) images into haemorrhage or nonhaemorrhage images. The aim of employing the deep learning model is to address the primary question in medical image analysis and classification: can a sufficient fine-tuning of a pretrained model (transfer learning) eliminate the need of building a CNN from scratch? Moreover, this study also aims to investigate the advantages of using SVM as a classifier instead of a three-layer neural network. We apply the same classification task to three deep networks; one is created from scratch, another is a pretrained model that was fine-tuned to the brain CT haemorrhage classification task, and our modified novel AlexNet model which uses the SVM classifier. The three networks were trained using the same number of brain CT images available. The experiments show that the transfer of knowledge from natural images to medical images classification is possible. In addition, our results proved that the proposed modified pretrained model “AlexNet-SVM” can outperform a convolutional neural network created from scratch and the original AlexNet in identifying the brain haemorrhage.

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Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

Jurnal ULTIMATICS ◽

10.31937/ti.v10i1.842 ◽

2018 ◽

Vol 10 (1) ◽

pp. 57-64 ◽

Cited By ~ 1

Author(s):

Rizqa Raaiqa Bintana ◽

Chastine Fatichah ◽

Diana Purwitasari

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Question Answering ◽

Mean Average Precision ◽

Average Precision ◽

Community Based ◽

Network Methods ◽

The Mean ◽

Index Terms ◽

Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

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Geometric property-based convolutional neural network for indoor object detection

International Journal of Advanced Robotic Systems ◽

10.1177/1729881421993323 ◽

2021 ◽

Vol 18 (1) ◽

pp. 172988142199332

Author(s):

Xintao Ding ◽

Boquan Li ◽

Jinbao Wang

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Geometric Property ◽

Ground Truth ◽

Geometric Constraints ◽

Depth Information ◽

Training Set ◽

Object Knowledge ◽

The Mean

Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropriate anchors for indoor objects. After the anchors are regressed to the regions of interest produced by a region proposal network (RPN-RoIs), we then use 2D geometric constraints to refine the RPN-RoIs, in which the 2D constraint of every classification is a convex hull region enclosing the width and height coordinates of the ground-truth boxes on the training set. Comparison experiments are implemented on two indoor datasets SUN2012 and NYUv2. Since the depth information is available in NYUv2, we involve depth constraints in GP-Faster and propose 3D geometric property-based Faster R-CNN (DGP-Faster) on NYUv2. The experimental results show that both GP-Faster and DGP-Faster increase the performance of the mean average precision.

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