Unsupervised end-to-end Brain Tumor Magnetic Resonance Image Registration using RBCNN: Rigid Transformation, B-Spline Transformation and Convolutional Neural Network

2021 ◽  
Vol 17 ◽  
Author(s):  
Senthil Pandi Sankareswaran ◽  
Mahadevan Krishnan
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Image Registration ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Computer Assisted ◽  
Rigid Transformation ◽  
Medical Image Registration ◽  
B Spline

Background: Image registration is the process of aligning two or more images in a single coordinate. Now a days, medical image registration plays a significant role in computer assisted disease diagnosis, treatment, and surgery. The different modalities available in the medical image makes medical image registration as an essential step in Computer Assisted Diagnosis(CAD), Computer-Aided Therapy (CAT) and Computer-Assisted Surgery (CAS). Problem definition: Recently many learning based methods were employed for disease detection and classification but those methods were not suitable for real time due to delayed response and need of pre alignment,labeling. Method: The proposed research constructed a deep learning model with Rigid transform and B-Spline transform for medical image registration for an automatic brain tumour finding. The proposed research consists of two steps. First steps uses Rigid transformation based Convolutional Neural Network and the second step uses B-Spline transform based Convolutional Neural Network. The model is trained and tested with 3624 MR (Magnetic Resonance) images to assess the performance. The researchers believe that MR images helps in success the treatment of brain tumour people. Result: The result of the proposed method is compared with the Rigid Convolutional Neural Network (CNN), Rigid CNN + Thin-Plat Spline (TPS), Affine CNN, Voxel morph, ADMIR (Affine and Deformable Medical Image Registration) and ANT(Advanced Normalization Tools) using DICE score, average symmetric surface distance (ASD), and Hausdorff distance. Conclusion: The RBCNN model will help the physician to automatically detect and classify the brain tumor quickly(18 Sec) and efficiently with out doing any pre-alignment and labeling.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

scholarly journals Accelerating B-spline interpolation on GPUs: Application to medical image registration

2020 ◽  
Vol 193 ◽  
pp. 105431 ◽  
Author(s):  
Orestis Zachariadis ◽  
Andrea Teatini ◽  
Nitin Satpute ◽  
Juan Gómez-Luna ◽  
Onur Mutlu ◽  
...  
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Spline Interpolation ◽  
Medical Image Registration ◽  
B Spline

Rigid medical image registration using PCA neural network

2006 ◽  
Vol 69 (13-15) ◽  
pp. 1717-1722 ◽  
Author(s):  
Lifeng Shang ◽  
Jian Cheng Lv ◽  
Zhang Yi
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Medical Image ◽  
Medical Image Registration

scholarly journals Bayesian Fully Convolutional Networks for Brain Image Registration

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kunpeng Cui ◽  
Panpan Fu ◽  
Yinghao Li ◽  
Yusong Lin
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Medical Image ◽  
Medical Image Registration ◽  
Bayesian Neural Network ◽  
Registration Accuracy ◽  
Change Analysis ◽  
Geometric Transformations ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image processing, such as image comparison, data fusion, target recognition, and pathological change analysis. Existing registration methods only consider registration accuracy but largely neglect the uncertainty of registration results. In this work, a method based on the Bayesian fully convolutional neural network is proposed for nonrigid medical image registration. The proposed method can generate a geometric uncertainty map to calculate the uncertainty of registration results. This uncertainty can be interpreted as a confidence interval, which is essential for judging whether the source data are abnormal. Moreover, the proposed method introduces group normalization, which is conducive to the network convergence of the Bayesian neural network. Some representative learning-based image registration methods are compared with the proposed method on different image datasets. Experimental results show that the registration accuracy of the proposed method is better than that of the methods, and its antifolding performance is comparable to that of fast image registration and VoxelMorph. Furthermore, the proposed method can evaluate the uncertainty of registration results.

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