YOLO-Based Image Segmentation for the Diagnostic of Spondylolisthesis From Lumbar Spine X-Ray Images

Spondylolisthesis, a condition characterized by vertebral slippage, often results in pain and limited mobility. To enhance the detection of spondylolisthesis in X-ray images, we developed a YOLOv8-based model trained on a dataset of 10,616 images (AP and LA views). To address the variability in X-ray image quality, caused by factors such as machine variations, medical expertise, patient movement, and artifacts, we employed a comprehensive suite of image processing techniques to simulate a wide range of real-world scenarios. These techniques included histogram equalization to adjust image contrast, blurring to simulate motion artifacts, brightness and contrast adjustments to mimic machine settings, negative color transformations to replicate radiologist-specific viewing preferences, and power law transformations to simulate scenarios of improper patient positioning or machine configuration. By augmenting the dataset with these enhanced images, we significantly improved the model's ability to generalize and accurately detect spondylolisthesis in diverse clinical settings. Our model achieved the highest precision of 96.20% for five classes in AP images without image enhancement and 96.77% for six classes in LA images with enhancement. Furthermore, it exhibited a minimum Euclidean Distance of 0.1992 in five classes AP images without enhancement and 0.1695 in six classes LA images with enhancement. Finally, the model achieved the best Intersection over Union (IOU) of 94.15% in five classes AP images without enhancement and 95.09% in six classes LA images with enhancement.