Abstract
Accurate vertebrae segmentation from medical images plays an important role in clinical tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease, spondylolisthesis, and post-operative assessment. Although the structures of bone have high contrast in medical images, vertebrae segmentation is a challenging task due to its complex structure, abnormal spine curves, and unclear boundaries. In recent years, deep learning has been widely applied in the segmentation of vertebrae images. In this paper, towards a robust and automatic segmentation system, we present an overlapping patch-based convNet (OP-convNet) model for automatic vertebrae CT images segmentation. Due to the greater memory and processing costs associated with 3D convolutional neural networks, as well as the risk of over-fitting, we employ overlapping patches in segmentation tasks using 2D convNet. In the proposed vertebrae segmentation method, OP-convNet effectively keeps the local information contained in CT images. We divide CT image slices into equal-sized square overlapping patches and applied the RUS-function on these patches for class balancing to minimize computational requirements. Then, these patches are input into the model along with their corresponding ground truth patches. This method has been evaluated on publicly available CT images from the MICCAI CSI workshop challenge. The results indicate that OP-convNet has precision (PRE) of 90.1%, specificity (SPE) of 99.4%, accuracy (ACC) of 98.8%, F-score of 90.1% in terms of the patch-based classification accuracy, and BF-score of 90.2%, sensitivity (SEN) of 90.3%, Jaccard index (JAC) of 82.3%, dice similarity score (DSC) of 89.9% in terms of the segmentation accuracy that outperform previous methods across all metrics.
Highlights
Image segmentation is a process that converts raw medical image data into meaningful, spatially structured information, which is necessary for scientific discovery [1]
Vertebrae segmentation performance is evaluated by boundary F1 score (BF-score), sensitivity (SEN), Jaccard index (JAC) [53], and Dice similarity coefficient (DSC) [54]
The experimental results demonstrated that the U-Net did not perform well in segmentation due to a lack of local information and achieved 83.7% of Dice similarity coefficient (DSC), whereas the proposed OP-convNet performed significantly better (89.9% DSC)
Summary
Image segmentation is a process that converts raw medical image data into meaningful, spatially structured information, which is necessary for scientific discovery [1]. Vertebrae segmentation is a prerequisite step for automatic spine analysis. Automated analysis of spine faces positive diversity among different tomographic scans, including dedicated spine scanning and the abdomen, chest, and neck scans. It needs a generic vertebrae segmentation robust to a range of image resolutions and spine coverage. This requires that the vertebrae are visible clearly with their anatomical structure and show which spine section they belong to
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
