Title:Unsupervised End-to-End Brain Tumor Magnetic Resonance Image Registration Using RBCNN: Rigid Transformation, B-Spline Transformation and Convolutional Neural Network
Volume: 18
Author(s): Senthil Pandi Sankareswaran*Mahadevan Krishnan
Affiliation:
- Department of Computer Science and Engineering, Mohamed Sathak A. J. College of Engineering, Tamil Nadu, India
Keywords:
Medical Image Registrations, deep learning, rigid transformation, B-spline transform, convolutional neural network, brain tumor magnetic resonance images, advanced normalization tools.
Abstract: Background: Image registration is the process of aligning two or more images in a single
coordinate. Nowadays, medical image registration plays a significant role in computer-assisted
disease diagnosis, treatment, and surgery. The different modalities available in the medical image
make medical image registration 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
the need for pre-alignment and labeling.
Methods: 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. The first step 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 help in the success of the treatment of patients
with brain tumour.
Results: 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 without doing pre-alignment and labeling.