FractalSpiNet: Fractal-Based U-Net for Automatic Segmentation of Cervical Spinal Cord and MS Lesions in MRI

Abstract

The spinal cord is an important part of the central nervous system, responsible for transmitting nerve signals throughout the body. The cervical spinal cord contains eight nerve bundles located in the neck region of the spinal cord that transmit to the face and head region. For this reason, in addition to traditional methods of monitoring changes in the spinal cord region in routine clinical practice, spinal cord segmentation using innovative computer-based systems makes an important contribution to the understanding of disease progression. Lesions in the cervical spinal cord can be a symptom of several neurological diseases, especially demyelinating diseases such as multiple sclerosis (MS). The detection of lesions in the spinal cord is particularly important in diseases such as MS, which affect a wide age range and for which early diagnosis is crucial. Therefore, automated segmentation of the spinal cord to quantify spinal cord atrophy is critical for changes in the human spinal cord. In addition to clinical findings, magnetic resonance imaging (MRI) technologies have improved the quality of images for monitoring, diagnosing and determining the treatment protocol for MS lesions in the spinal cord. However, due to the difficulty of scanning the cervical spinal cord region and the occurrence of artefacts during acquisition, it is very difficult to determine the spinal cord boundaries and detect lesions in this region. In this study, we propose a fractal network-based U-Net (FractalSpiNet) deep learning architecture for automatic segmentation of the spinal cord and spinal cord MS lesions from cervical spinal cord MR slices. The developed FractalSpiNet architecture incorporate a fractal network for enhanced feature extraction in MRI scans. In addition, a new dataset of axial plane MR images from the cervical spinal cord of 87 MS patients is first created in the study. Using the proposed FractalSpiNet architecture, the cross-sectional area of the cervical spinal cord was segmented with a Dice Similarity Coefficient (DSC) score of 98.88%, while MS lesions in the cervical spinal cord were detected with a DSC score of 90.90%. These results indicate that FractalSpiNet provides results that close to expert mask for segmentation of cervical spinal cord and MS lesion detection. The experimental studies also compare the results of the proposed FractalSpiNet with the results of state-of-the-art hybrid U-Net models such as base U-Net, Attention U-Net, Residual U-Net, and Attention Residual U-Net. In conclusion, the experimental results demonstrate the effectiveness of our approach in achieving accurate segmentation of cervical spinal cord and MS lesions, outperforming stateof-the-art methods. The proposed FractalSpiNet offers a promising approach for automated segmentation of the cervical spinal cord and MS lesions, potentially aiding in the diagnosis and treatment of neurological disorders.