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Biomedical subjects

T Uthmann

Publications and source records attributed to T Uthmann.

6 recordsLinked to original sources
PubMed2001

[A software tool for automatic image-based ventilation analysis using dynamic chest CT-scanning in healthy and in ARDS lungs].

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 tested in 8 anaesthetized ventilated pigs undergoing dynamic CT before and after induction of lavage-ARDS. Images were acquired in one supradiaphragmatic, cross-sectional slice (temporal resolution of 100 ms; slice thickness of 1 mm, high resolution reconstruction algorithm). In 120 CT images the total pixel number and the calculated MLD from the automatically segmentated lung were compared to the values obtained from an interactive lung segmentation. RESULTS: The software tool was able to read all image series (DICOM standard). Automatic and interactive segmentation were in high agreement (R(2) = 0.99 for the total number of pixels and the MLD). Originally, the most frequent error was misclassification of atelectasis as extrapulmonary solid tissue. CONCLUSION: An automatic software tool is presented for lung segmentation in healthy lungs and in ARDS. Aerated lung and atelectasis were identified with high accuracy. This post-processing tool allows for a quantitative, CT based assessment of ventilation and recruitment processes in the lung. Thus, it may help to optimize ventilation patterns in patients with ARDS.

Animals↗
PubMed2001

A study of the simulated evolution of the spectral sensitivity of visual agent receptors.

In this article we study a model for the evolution of the spectral sensitivity of visual receptors for agents in a continuous virtual environment. The model uses a genetic algorithm (GA) to evolve the agent sensors along with the control of the agents by requiring the agents to solve certain tasks in the simulation environment. The properties of the evolved sensors are analyzed for different scenarios. In particular, it is shown that the GA is able to find a balance between sensor costs and agent performance in such a way that the spectral sensor sensitivity reflects the emission spectrum of the target objects and that the capability of the sensors to evolve can help the agents significantly in adapting to their task.

Algorithms↗
PubMed2000

[Automated detection of spleen volume by spiral CT scans using neural networks and "fuzzy logic"].

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.

Adult↗
PubMed2000

Automatic detection and quantification of ground-glass opacities on high-resolution CT using multiple neural networks: comparison with a density mask.

OBJECTIVE: We compared multiple neural networks with a density mask for the automatic detection and quantification of ground-glass opacities on high-resolution CT under clinical conditions. SUBJECTS AND METHODS: Eighty-four patients (54 men and 30 women; age range, 18-82 years; mean age, 49 years) with a total of 99 consecutive high-resolution CT scans were enrolled in the study. The neural network was designed to detect ground-glass opacities with high sensitivity and to omit air-tissue interfaces to increase specificity. The results of the neural network were compared with those of a density mask (thresholds, -750/-300 H), with a radiologist serving as the gold standard. RESULTS: The neural network classified 6% of the total lung area as ground-glass opacities. The density mask failed to detect 1.3%, and this percentage represented the increase in sensitivity that was achieved by the neural network. The density mask identified another 17.3% of the total lung area to be ground-glass opacities that were not detected by the neural network. This area represented the increase in specificity achieved by the neural network. Related to the extent of the ground-glass opacities as classified by the radiologist, the neural network (density mask) reached a sensitivity of 99% (89%), specificity of 83% (55%), positive predictive value of 78% (18%), negative predictive value of 99% (98%), and accuracy of 89% (58%). CONCLUSION: Automatic segmentation and quantification of ground-glass opacities on high-resolution CT by a neural network are sufficiently accurate to be implemented for the preinterpretation of images in a clinical environment; it is superior to a double-threshold density mask.

Absorptiometry, Photon↗
PubMed1997

Automatic detection of ground glass opacities on lung HRCT using multiple neural networks.

The purpose of this study was to implement neural networks and expert rules for the automatic detection of ground glass opacities (GG) on high-resolution computed tomography (HRCT). Different approaches using self-organizing neural nets as well as classifications of lung HRCT with and without the use of explicit textural parameters have been applied in preliminary studies. In the present study a hybrid network of three single nets and an expert rule was applied for the detection of GG on 120 HRCT scans from 20 patients suffering from different lung diseases. Single nets alone were not capable to reliably detect or exclude GG since the false-positive rate was greater than 100 % with regard to the area truly involved, more than 50 pixels throughout, and the true-positive rate was greater than 95 %. The hybrid network correctly classified 91 of 120 scans. Mild GG was false positive in 15 cases with less than 50 pixels, which was judged not clinically relevant. The pitfalls were: partial volume effects of bronchovascular bundles and the chest wall. Motion artefacts and diaphragm were responsible for 11 misclassifications. Hybrid networks represent a promising tool for an automatic pathology-detecting system. They are ready to use as a diagnostic assistant for detection, quantification and follow-up of ground glass opacities, and further applications are underway.

Expert Systems↗