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Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI
May 8, 2024, 4:46 a.m. | Rikathi Pal, Sudeshna Mondal, Aditi Gupta, Priya Saha, Somoballi Ghoshal, Amlan Chakrabarti, Susmita Sur-Kolay
cs.CV updates on arXiv.org arxiv.org
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
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