Optimal Net Surface Problems with Applications
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Electric Field Theory Motivated Graph Construction for Optimal Medical Image Segmentation
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Optimal graph based segmentation using flow lines with application to airway wall segmentation
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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Lung cancer is the most common cause of cancer-related death. A common treatment is radiotherapy where the lung tumors are irradiated with ionizing radiation. The treatment is typically fractionated, i.e. spread out over time, allowing healthy tissue to recover between treatments and allowing tumor cells to be hit in their most sensitive phase. Changes in tumors over the course of treatment allows for an adaptation of the radiotherapy plan based on 3D computer tomography imaging. This paper introduces a method for segmentation of lung tumors on consecutive computed tomography images. These images are normally only used for correction of movements. The method uses graphs based on electric flow lines. The method offers several advantages when trying to replicate manual segmentations. The method gave a dice coefficient of 0.85 and performed better than level set methods and deformable registration.