  In medical imaging, segmentation and localization of spinal tumors in
three-dimensional (3D) space pose significant computational challenges,
primarily stemming from limited data availability. In response, this study
introduces a novel data augmentation technique, aimed at automating spine tumor
segmentation and localization through AI approaches. Leveraging a fusion of
fuzzy c-means clustering and Random Forest algorithms, the proposed method
achieves successful spine tumor segmentation based on predefined masks
initially delineated by domain experts in medical imaging. Subsequently, a
Convolutional Neural Network (CNN) architecture is employed for tumor
classification. Moreover, 3D vertebral segmentation and labeling techniques are
used to help pinpoint the exact location of the tumors in the lumbar spine.
Results indicate a remarkable performance, with 99% accuracy for tumor
segmentation, 98% accuracy for tumor classification, and 99% accuracy for tumor
localization achieved with the proposed approach. These metrics surpass the
efficacy of existing state-of-the-art techniques, as evidenced by superior Dice
Score, Class Accuracy, and Intersection over Union (IOU) on class accuracy
metrics. This innovative methodology holds promise for enhancing the diagnostic
capabilities in detecting and characterizing spinal tumors, thereby
facilitating more effective clinical decision-making.
