Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
A work-efficient GPU algorithm for level set segmentation
Proceedings of the Conference on High Performance Graphics
MRI-based finite element simulation on radiofrequency ablation of thyroid cancer
Computer Methods and Programs in Biomedicine
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Precision and accuracy are sometimes sacrificed to ensure that medical image processing is rapid. To address this, our lab had developed a novel level set segmentation algorithm that is 16x faster and 96% accurate on realistic brain phantoms. Methods: This study reports speed, precision and estimated accuracy of our algorithm when measuring MRIs of meningioma brain tumors and compares it to manual tracing and modified MacDonald (MM) ellipsoid criteria. A repeated-measures study allowed us to determine measurement precisions (MPs) - clinically relevant thresholds for statistically significant change. Results: Speed: the level set, MM, and trace methods required 1:20, 1:35, and 9:35 (mm:ss) respectively on average to complete a volume measurement (p0.05). Precision: the MM's within-operator and between-operator MPs were significantly higher (worse) than the other methods (p0.05). Conclusion: Our level set is faster on average than MM, yet has accuracy and precision comparable to manual tracing.