Spine Medical Image Segmentation Based on Deep Learning

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 ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>P</a:mi> <a:mo>&lt;</a:mo> <a:mn>0.05</a:mn> </a:math> ). 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 ( <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>P</c:mi> <c:mo>&lt;</c:mo> <c:mn>0.05</c:mn> </c:math> ). 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.