Enhanced Spine Segmentation in Scoliosis X-ray Images via U-Net

Abstract

Scoliosis prevalence is witnessing an upward trend, rendering image segmentation an invaluable tool in appraising the condition's severity. The segmentation of spinal images, however, poses notable challenges primarily due to the image quality and the complexity of discerning the Region of Interest (ROI) on X-ray imagery. This difficulty arises from the uniform texture and luminosity of the background, complicating the ROI detection process. Our study investigates the performance of U-Net in image segmentation using anteriorposterior X-ray imagery of spines afflicted with scoliosis. A corpus of 609 high-resolution images was assembled for this purpose, partitioned into 481 training and 128 testing images. Prior to model implementation, a data augmentation process was carried out to bolster the training datasets, mitigating the risk of model overfitting. The augmentation involved mirroring and adding black and white intensity to each image, thereby generating thirteen new images from each original image. This process amplified the size of the training dataset from 481 to 6734 images. Our findings validate the efficacy of the U-Net model in accurately segmenting the spine in X-ray images, demonstrating an accuracy of 97% in training and 94% in validation, with a corresponding loss of 0.063 and validation loss of 0.16. The resultant segmentation is poised to enhance the precision of scoliosis severity assessment.

Image Segmentation in Complex Backgrounds using an Improved Generative Adversarial Network

“…As technology progresses, image processing technology has been widely applied, especially in the field of image segmentation in complex backgrounds, which is a key technology in many areas including medical imaging, intelligent video surveillance, and machine vision [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. However, image segmentation in complex backgrounds remains a challenging research problem, as features in images may become difficult to recognize due to factors such as lighting, texture, and color in complicated environments [18][19][20].…”

Automated Identification and Classification of Brain Tumors Using Hybrid Machine Learning Models and MRI Imaging

“…Significant research efforts have been channeled into segmentation in medical imaging, including cancer detection in the brain, lungs and breasts [6].…”