MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
A Statistical Approach for Estimating Brain Tumor-Induced Deformation
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Deformable registration of brain tumor images via a statistical model of tumor-induced deformation
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Dense deformation field estimation for atlas-based segmentation of pathological MR brain images
Computer Methods and Programs in Biomedicine
A coupled finite element model of tumor growth and vascularization
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Modeling glioma growth and mass effect in 3D MR images of the brain
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
FEM based 3D tumor growth prediction for kidney tumor
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Deformable registration of brain tumor images via a statistical model of tumor-induced deformation
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
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Motivated by the need for methods to aid the deformable registration of brain tumor images, we present a three-dimensional (3D) mechanical model for simulating large non-linear deformations induced by tumors to the surrounding encephalic tissues. The model is initialized with 3D radiological images and is implemented using the finite element (FE) method. To simulate the widely varying behavior of brain tumors, the model is controlled by a number of parameters that are related to variables such as the bulk tumor location, size, mass-effect, and peri-tumor edema extent. Model predictions are compared to real brain tumor-induced deformations observed in serial-time MRI scans of a human subject and 3 canines with surgically transplanted gliomas. Results indicate that the model can reproduce the real deformations with an accuracy that is similar to that of manual placement of landmark points to which the model is compared.