0000000000364946
AUTHOR
K. R. Heitmann
Software zur automatischen Quantifizierung von Belüftungszuständen bei akutem Lungenversagen in dynamischen CT-Aufnahmen der Lunge1
Purpose: Density measurements in dynamic CT image series of the lungs allow one to quantify ventilated, hyperinflated, and atelectatic pulmonary compartments with high temporal resolution. Fast automatic segmentation of lung parenchyma and a subsequent evaluation of it's respective density values are a prerequisite for any clinical application of this technique. Material and Methods: For automatic lung segmentation in thoracic CT scans, an algorithm was developed which uses (a) different density masks, and (b) anatomic knowledge to differentiate heart, diaphragm and chest wall from ventilated and atelectatic lung parenchyma. With Animal Care Committee approval, the automated technique was t…
Selbstorganisierende neuronale Netze zur automatischen Detektion und Klassifikation von Kontrast(mittel)-verstärkten Läsionen in der dynamischen MR-Mammographie
Purpose: Investigation and statistical evaluation of "Self-Organizing Maps," a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. Material and Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-…
Automatische Berechnung des Milzvolumens aus Spiral-CT-Daten mit Hilfe neuronaler Netze und „Fuzzy Logik”∗
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. Cor…