ArticleJournal of medical systems2012
A medical software system for volumetric analysis of cerebral pathologies in magnetic resonance imaging (MRI) data.
Article in Journal of medical systems, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Computed tomography data collection of the complete human mandible and valid clinical ground truth models.Scientific data · 2019Article
- Interactive Outlining of Pancreatic Cancer Liver Metastases in Ultrasound Images.Scientific reports · 2017Article
- Interactive Volumetry Of Liver Ablation Zones.Scientific reports · 2015Article
- Classification of hemodynamic responses associated with force and speed imagery for a brain-computer interface.Journal of medical systems · 2015Article
- Analysis of enlarged images using time-of-flight magnetic resonance angiography, computed tomography, and conventional angiography.Journal of medical systems · 2014Article
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- GBM volumetry using the 3D Slicer medical image computing platform.Scientific reports · 2013Article
- Manual refinement system for graph-based segmentation results in the medical domain.Journal of medical systems · 2012Article
- Pituitary adenoma volumetry with 3D Slicer.PloS one · 2012Article
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- Template-cut: a pattern-based segmentation paradigm.Scientific reports · 2012Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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