RATIONALE AND OBJECTIVES: Medical image segmentation is still very time consuming and is therefore seldom integrated into clinical routine. Various three-dimensional (3D) segmentation approaches could facilitate the work, but they are rarely used in clinical setups because of complex initialization and parametrization of such models. MATERIALS AND METHODS: We developed a new semiautomatic 3D-segmentation tool based on deformable simplex meshes. The user can define attracting points in the original image data. The new deformation algorithm guarantees that the surface model will pass through these interactively set points. The user can directly influence the evolution of the deformable model and gets direct feedback during the segmentation process. RESULTS: The segmentation tool was evaluated for cardiac image data and magnetic resonance imaging lung images. Comparison with manual segmentation showed high accuracy. Time needed for delineation of the various structures could be reduced in some cases. The model was not sensitive to noise in the input data and model initialization. CONCLUSIONS: The tool is suitable for fast interactive segmentation of any kind of 3D or 3D time-resolved medical image data. It enables the clinician to influence a complex 3D-segmentation algorithm and makes this algorithm controllable. The better the quality of the data, the less interaction is required. The tool still works when the processed images have low quality.
RATIONALE AND OBJECTIVES: Medical image segmentation is still very time consuming and is therefore seldom integrated into clinical routine. Various three-dimensional (3D) segmentation approaches could facilitate the work, but they are rarely used in clinical setups because of complex initialization and parametrization of such models. MATERIALS AND METHODS: We developed a new semiautomatic 3D-segmentation tool based on deformable simplex meshes. The user can define attracting points in the original image data. The new deformation algorithm guarantees that the surface model will pass through these interactively set points. The user can directly influence the evolution of the deformable model and gets direct feedback during the segmentation process. RESULTS: The segmentation tool was evaluated for cardiac image data and magnetic resonance imaging lung images. Comparison with manual segmentation showed high accuracy. Time needed for delineation of the various structures could be reduced in some cases. The model was not sensitive to noise in the input data and model initialization. CONCLUSIONS: The tool is suitable for fast interactive segmentation of any kind of 3D or 3D time-resolved medical image data. It enables the clinician to influence a complex 3D-segmentation algorithm and makes this algorithm controllable. The better the quality of the data, the less interaction is required. The tool still works when the processed images have low quality.
Authors: Vanessa K Tidwell; Joel R Garbow; Alexander S Krupnick; John A Engelbach; Arye Nehorai Journal: Magn Reson Med Date: 2011-09-27 Impact factor: 4.668
Authors: William Kovacs; Nathan Hsieh; Holger Roth; Chioma Nnamdi-Emeratom; W Patricia Bandettini; Andrew Arai; Ami Mankodi; Ronald M Summers; Jianhua Yao Journal: J Med Imaging (Bellingham) Date: 2017-11-30
Authors: P F Villard; F P Vidal; L ap Cenydd; R Holbrey; S Pisharody; S Johnson; A Bulpitt; N W John; F Bello; D Gould Journal: Int J Comput Assist Radiol Surg Date: 2013-07-24 Impact factor: 2.924