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

TL;DR

This paper presents an AI-based method combining data augmentation, fuzzy c-means, Random Forest, and CNNs for accurate lumbar spine tumor segmentation and localization in T2 MRI images, outperforming existing techniques.

Contribution

Introduces a novel AI approach with data augmentation and fusion of clustering, classification, and segmentation techniques for improved spinal tumor detection.

Findings

99% accuracy in tumor segmentation

98% accuracy in tumor classification

99% accuracy in tumor localization

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. 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.