6533b830fe1ef96bd1296f54
RESEARCH PRODUCT
Automatische Berechnung des Milzvolumens aus Spiral-CT-Daten mit Hilfe neuronaler Netze und „Fuzzy Logik”∗
K. R. HeitmannHu KauczorManfred ThelenCp HeusselRückert SThomas Uthmannsubject
Spiral CT Scansbusiness.industryRegion growingMedicineRadiology Nuclear Medicine and imagingSegmentationFalse positive rateImage analysisSpiral ctbusinessNuclear medicineTrue positive rateThresholdingdescription
PURPOSE To assess spleen segmentation and volumentry in spiral CT scans with and without pathological changes of splenic tissue. METHODS The image analysis software HYBRIKON is based on region growing, self-organized neural nets, and fuzzy-anatomic rules. The neural nets were trained with spiral CT data from 10 patients, not used in the following evaluation on spiral CT scans from 19 patients. An experienced radiologist verified the results. The true positive and false positive areas were compared in terms to the areas marked by the radiologist. The results were compared with a standard thresholding method. RESULTS The neural nets achieved a higher accuracy than the thresholding method. Correlation coefficient of the fuzzy-neural nets: 0.99 (thresholding: 0.63). Mean true positive rate: 90% (thresholding: 75%), mean false positive rate: 5% (thresholding > 100%). Pitfalls were caused by accessory spleens, extreme changes in the morphology (tumors, metastases, cysts), and parasplenic masses. CONCLUSIONS Self-organizing neural nets combined with fuzzy rules are ready for use in the automatic detection and volumetry of the spleen in spiral CT scans.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2000-02-01 | RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren |