Abstract
The aim was to further explore the clinical value of deep learning algorithm in the field of spinal medical image segmentation, and this study designed an improved U-shaped network (BN-U-Net) algorithm and applied it to the spinal MRI medical image segmentation of 22 research objects. The application value of this algorithm in MRI image processing was comprehensively evaluated by accuracy (Acc), sensitivity (Sen), specificity (Spe), and area under curve (AUC). The results show that the image processing time of fully convolutional network (FCN) algorithm and U-Net algorithm is greater than 6 min, while the processing time of BN-U-Net algorithm is only 5–10 s, and the processing time is significantly shortened (P < 0.05). The Acc, Sen, and Spe results of BN-U-Net segmentation algorithm were 94.54 ± 3.56%, 88.76 ± 2.67%, and 86.27 ± 6.23%, respectively, which were significantly improved compared with FCN algorithm and U-Net algorithm (P < 0.05). In summary, the improved U-Net network algorithm used in this study significantly improves the quality of spinal MRI images by automatic segmentation of MRI images, which is worthy of further promotion in the field of spinal medical image segmentation.
Highlights
At present, MRI technology is one of the most commonly used imaging technologies in clinical spine examination [5]
It can be seen from the figure that the gray level of the edge and surrounding of the probability prediction map is slightly lighter than that of other parts, which indicates that the pixels in this part are likely to belong to the spine, the gray level of the surrounding background is slightly deeper than that of this part, and the background part may be on spine. It shows that the spinal MRI image segmented by this algorithm can clearly distinguish the spinal and nonspinal parts, and the automatic segmentation algorithm constructed in this study is very close to the real segmented image and has high similarity
Segmentation Results of Spinal MRI Images Based on Deep Learning Algorithm. ree groups of representative spinal MRI images are randomly selected from the dataset used in the study, and these images are input into the network model constructed in this study for segmentation. e
Summary
MRI technology is one of the most commonly used imaging technologies in clinical spine examination [5]. Deep learning technology has been widely used in the segmentation of various medical images. Various deep learning algorithms emerge endlessly, which make the quality of medical images significantly improved. The semiautomatic segmentation of spinal MRI images is realized by deep learning algorithm to improve the edge blur and type matching limitation of conventional MRI images. In view of the obvious difference between the gray levels of intervertebral disc and vertebra in MRI images, the noninitial state set model is added to the spinal medical image, so as to realize the effective segmentation of intervertebral disc MRI images [11]. By constructing the network model, this research explores the MRI image features and effects processed by deepening the U-Net network image segmentation algorithm, to provide more reference for doctors in clinical practice
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.
