Automatic Segmentation of Spinal Canals in CT Images via Iterative Topology Refinement

Spinal cord segmentation and injury detection using a Crow Search-Rider optimization algorithm

The damage in the spinal cord due to vertebral fractures may result in loss of sensation and muscle function either permanently or temporarily. The neurological condition of the patient can be improved only with the early detection and the treatment of the injury in the spinal cord. This paper proposes a spinal cord segmentation and injury detection system based on the proposed Crow search-Rider Optimization-based DCNN (CS-ROA DCNN) method, which can detect the injury in the spinal cord in an effective manner. Initially, the segmentation of the CT image of the spinal cord is performed using the adaptive thresholding method, followed by which the localization of the disc is performed using the Sparse FCM clustering algorithm (Sparse-FCM). The localized discs are subjected to a feature extraction process, where the features necessary for the classification process are extracted. The classification process is done using DCNN trained using the proposed CS-ROA, which is the integration of the Crow Search Algorithm (CSA) and Rider Optimization Algorithm (ROA). The experimentation is performed using the evaluation metrics, such as accuracy, sensitivity, and specificity. The proposed method achieved the high accuracy, sensitivity, and specificity of 0.874, 0.8961, and 0.8828, respectively that shows the effectiveness of the proposed CS-ROA DCNN method in spinal cord injury detection.

Injury classification and level detection of the spinal cord based on the optimized recurrent neural network

Objectives

Spinal cord damage is one of the traumatic situations in persons that may cause the loss of sensation and proper functioning of the muscles either temporarily or permanently. Hence, steps to assure the recovery through the early functioning and precaution could safe-guard a proper interceptive. To ensure the recovery of spinal cord damage through optimized recurrent neural network.

Methods

The research on the spinal cord injury classification and level detection is done using the CT images, which is initially given to the segmentation that is done using the adaptive thresholding methodology. Once the segments are formed, the disc is localized using the sparse fuzzy C-means clustering approach. In the next step, the features are extracted from the localized disc and the features include the connectivity features, statistical features, image-level features, grid-level features, Histogram of Oriented Gradients (HOG), and Linear Gradient Pattern (LGP). Then, the injury detection is done based on the Crow search Rider Optimization algorithm-based Deep Convolutional Neural Network (CS-ROA-based DCNN). Once the result regarding the presence of the injury is obtained, the injury-level classification is done based on the proposed Deep Recurrent Neural Network (Deep RNN), and in case of the absence of injury, the process is terminated. Therefore, the injury detection classifier derives the level of the injury, such as normal, wedge, biconcavity, and crush.

Results

The experimentation is carried out using an Osteoporotic vertebral fractures database. The performance of the injury level detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 0.895, maximal sensitivity of 0.871, and the maximal specificity of 0.933 with respect to K-Fold.

Conclusions

The experimental results show that the proposed model is better than the existing models in terms of accuracy, sensitivity, and specificity.

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images.