Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Deformable Organisms for Automatic Medical Image Analysis
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Intensity Ridge and Widths for Tubular Object Segmentation and Description
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Vessel Crawlers: 3D Physically-based Deformable Organisms for Vasculature Segmentation and Analysis
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Computer-Aided Assessment of Anomalies in the Scoliotic Spine in 3-D MRI Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Automatic segmentation of spinal cord mri using symmetric boundary tracing
IEEE Transactions on Information Technology in Biomedicine
Gradient competition anisotropy for centerline extraction and segmentation of spinal cords
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Spinal cord analysis is an important problem relating to the study of various neurological diseases. We present a novel approach to spinal cord segmentation in magnetic resonance images. Our method uses 3D “deformable organisms” (DefOrg) an artificial life framework for medical image analysis that complements classical deformable models (snakes and deformable meshes) with high-level, anatomically-driven control mechanisms. The DefOrg framework allows us to model the organism’s body as a growing generalized tubular spring-mass system with an adaptive and predominantly elliptical cross section, and to equip them with spinal cord specific sensory modules, behavioral routines and decision making strategies. The result is a new breed of robust DefOrgs, “spinal crawlers”, that crawl along spinal cords in 3D images, accurately segmenting boundaries, and providing sophisticated, clinically-relevant structural analysis. We validate our method through the segmentation of spinal cords in clinical data and provide comparisons to other segmentation techniques.