Automatic segmentation of rodent spinal cord diffusion MR images

A deep learning approach for cervical cord injury severity determination through axial and sagittal magnetic resonance imaging segmentation and classification

STUDY DESIGN

Cross-sectional Database Study.

OBJECTIVE

While the American Spinal Injury Association (ASIA) Impairment Scale is the standard for assessing spinal cord injuries (SCI), it has limitations due to subjectivity and impracticality. Advances in machine learning (ML) and image recognition have spurred research into their use for outcome prediction. This study aims to analyze deep learning techniques for identifying and classifying cervical SCI severity from MRI scans.

METHODS

The study included patients with traumatic and nontraumatic cervical SCI admitted from 2019 to 2022. MRI images were labeled by two senior resident physicians. A deep convolutional neural network was trained using axial and sagittal cervical MRI images from the dataset. Model performance was assessed using Dice Score and IoU to measure segmentation accuracy by comparing predicted and ground truth masks. Classification accuracy was evaluated with the F1 Score, balancing false positives and negatives.

RESULT

In the axial spinal cord segmentation, we achieved a Dice score of 0.94 for and IoU score of 0.89. In the sagittal spinal cord segmentation, we obtained Dice score up to 0.9201 and IoU scores up to 0.8541. The model for axial image score classification gave a satisfactory result with an F1 score of 0.72 and AUC of 0.79.

CONCLUSION

Our models successfully identified cervical SCI on T2-weighted MR images with satisfactory performance. Further research is needed to develop more advanced models for predicting patient outcomes in SCI cases.

Quantifying Intraparenchymal Hemorrhage after Traumatic Spinal Cord Injury: A Review of Methodology

Intraparenchymal hemorrhage (IPH) after a traumatic injury has been associated with poor neurological outcomes. Although IPH may result from the initial mechanical trauma, the blood and its breakdown products have potentially deleterious effects. Further, the degree of IPH has been correlated with injury severity and the extent of subsequent recovery. Therefore, accurate evaluation and quantification of IPH following traumatic spinal cord injury (SCI) is important to define treatments' effects on IPH progression and secondary neuronal injury. Imaging modalities, such as magnetic resonance imaging (MRI) and ultrasound (US), have been explored by researchers for the detection and quantification of IPH following SCI. Both quantitative and semiquantitative MRI and US measurements have been applied to objectively assess IPH following SCI, but the optimal methods for doing so are not well established. Studies in animal SCI models (rodent and porcine) have explored US and histological techniques in evaluating SCI and have demonstrated the potential to detect and quantify IPH. Newer techniques using machine learning algorithms (such as convolutional neural networks [CNN]) have also been studied to calculate IPH volume and have yielded promising results. Despite long-standing recognition of the potential pathological significance of IPH within the spinal cord, quantifying IPH with MRI or US is a relatively new area of research. Further studies are warranted to investigate their potential use. Here, we review the different and emerging quantitative MRI, US, and histological approaches used to detect and quantify IPH following SCI.

A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of Parkinson's disease

Brain MR images are composed of three main regions such as gray matter, white matter and cerebrospinal fluid. Radiologists and medical practitioners make decisions through evaluating the developments in these regions. Study of these MR images suffers from two major issues such as: (a) the boundaries of their gray matter and white matter regions are ambiguous and unclear in nature, and (b) their regions are formed with unclear inhomogeneous gray structures. These two issues make the diagnosis of critical diseases very complex. To solve these issues, this study presented a method of image segmentation based on the neutrosophic set (NS) theory and neutrosophic entropy information (NEI). By nature, the proposed method is adaptive to select the threshold value and is entitled as neutrosophic-entropy based adaptive thresholding segmentation algorithm (NEATSA). In this study, experimental results were provided through the segmentation of Parkinson's disease (PD) MR images. Experimental results, including statistical analyses showed that NEATSA can segment the main regions of MR images very clearly compared to the well-known methods of image segmentation available in literature of pattern recognition and computer vision domains.