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14 February 2012 Semi-automatic intracranial tumor segmentation and tumor tissue classification based on multiple MR protocols
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Segmentation of intracranial tumors in Magnetic Resonance (MR) data sets and classification of the tumor tissue into vital, necrotic, and perifocal edematous areas is required in a variety of clinical applications. Manual delineation of the tumor tissue boundaries is a tedious and error-prone task, and reproducibility is problematic. Furthermore, tissue classification mostly requires information of several MR protocols and contrasts. Here we present a nearly automatic segmentation and classification algorithm for intracranial tumor tissue working on a combination of T1 weighted contrast enhanced (T1CE) and fluid attenuated inversion recovery (FLAIR) data sets. Both data types are included in MR intracranial tumor protocols that are used in clinical routine. The algorithm is based on a region growing technique. The main required user interaction is a mouse click to provide the starting point. The region growing thresholds are automatically adapted to the requirements of the actual data sets. If the segmentation result is not fully satisfying, the user is allowed to adapt the algorithmic parameters for final fine-tuning. We developed a user interface, where the data sets can be loaded, the segmentation can be started by a mouse click, the parameters can be amended, and the segmentation results can be saved. With this user interface, our segmentation tool can be used in the hospital on an image processing workstation or even directly on the MR scanner. This enables an extensive validation study. On the 20 clinical test cases of human intracranial tumors we investigated so far, the results were satisfying in 85% of the cases.
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A. Franz, H. Tschampa, A. Müller, S. Remmele, C. Stehning, J. Keupp, J. Gieseke, H. H. Schild, and P. Mürtz "Semi-automatic intracranial tumor segmentation and tumor tissue classification based on multiple MR protocols", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142K (14 February 2012);

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