A Medical Software System for Volumetric Analysis of Cerebral Pathologies in Magnetic Resonance Imaging (MRI) Data

Department of Neurosurgery, University of Marburg, Baldingerstraße, Marburg, Germany.
Journal of Medical Systems (Impact Factor: 2.21). 03/2011; 36(4):2097-109. DOI: 10.1007/s10916-011-9673-6
Source: PubMed


In this contribution, a medical software system for volumetric analysis of different cerebral pathologies in magnetic resonance imaging (MRI) data is presented. The software system is based on a semi-automatic segmentation algorithm and helps to overcome the time-consuming process of volume determination during monitoring of a patient. After imaging, the parameter settings-including a seed point-are set up in the system and an automatic segmentation is performed by a novel graph-based approach. Manually reviewing the result leads to reseeding, adding seed points or an automatic surface mesh generation. The mesh is saved for monitoring the patient and for comparisons with follow-up scans. Based on the mesh, the system performs a voxelization and volume calculation, which leads to diagnosis and therefore further treatment decisions. The overall system has been tested with different cerebral pathologies-glioblastoma multiforme, pituitary adenomas and cerebral aneurysms- and evaluated against manual expert segmentations using the Dice Similarity Coefficient (DSC). Additionally, intra-physician segmentations have been performed to provide a quality measure for the presented system.

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    • "To quantify the accuracy of gray matter segmentations, we used the Dice coefficient (DC) [35], a similarity measure related to the Jaccard index. The DC is commonly used to determine accuracy of segmentation methods in neuroimaging settings [36], [37], [38] and is defined as the size of the union of the segmentation result and the ground truth: DC = 2TP/((FP + TP) + (TP + FN)), that is, the set of True Positives (TP) is divided by the average size of the segmentation result (False Positives (FP) + True Positives (TP)) and the ground truth (True Positives (TP) + False Negatives (FN)). A DC of 0 indicates no overlap; a value of 1 indicates perfect agreement. "
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