Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI

arXiv:2405.04023v1 Announce Type: cross
Abstract: 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. …

abstract arxiv augmentation availability challenges computational cs.cv data eess.iv images imaging localization medical medical imaging mri novel segmentation space stemming study three-dimensional through tumors type