Biomedical subjects

Ulrich Scheipers

Publications and source records attributed to Ulrich Scheipers.

4 recordsLinked to original sources

PubMed2005

Sonohistology for the computerized differentiation of parotid gland tumors.

A system for the computerized differentiation of parotid gland tumors is proposed. The parotid gland is the largest of the salivary glands. It is found in the subcutaneous tissue of the face, overlying the mandibular ramus and anterior and inferior to the external ear. The classification system is based on a multifeature tissue characterization approach, using fuzzy inference systems as higher-order classifiers. Baseband ultrasonic echo data were acquired during conventional ultrasound imaging examinations using standard ultrasound equipment. Several tissue-describing parameters were calculated within numerous small regions of interest to evaluate spectral and textural tissue properties. The parameters were processed by an adaptive network-based fuzzy inference system, using the results of conventional histology after parotidectomy as the "gold standard." The results of the classification are presented as a numerical score indicating the probability of a certain tumor or alteration for each parotid gland. The score can be presented to the physician during examination of the patient to improve the differentiation between various types of parotid gland tumors. The system was evaluated on n = 23 cases of patients undergoing radical parotidectomy. The receiver operating characteristic curve area is A(ROC) = 0.95 +/- 0.07 when using fourfold cross-validation over cases and differentiating between various benign parotid gland tumors and monomorphic adenoma.

Adenoma↗

PubMed2005

Initial experiences with real-time elastography guided biopsies of the prostate.

PURPOSE: Based on our first experiences with real-time elastography in the field of prostate diagnostics we evaluate its usefulness for biopsy guidance for prostate cancer detection. MATERIALS AND METHODS: After imaging with conventional B-mode ultrasound in conjunction with real-time elastography 404 men underwent systematic sextant biopsy. RESULTS: Overall prostate cancer was found in 151 of 404 cases (37.4%). In 127 of 151 cases (84.1%), prostate cancer was detected using real-time elastography as an additional diagnostic feature. CONCLUSIONS: The results show that it is possible to detect prostate cancer with a high degree of sensitivity using real-time elastography in conjunction with conventional diagnostic methods for guided prostate biopsies.

Aged↗

PubMed2005

A tutorial on the use of ROC analysis for computer-aided diagnostic systems.

The application of the receiver operating characteristic (ROC) curve for computer-aided diagnostic systems is reviewed. A statistical framework is presented and different methods of evaluating the classification performance of computer-aided diagnostic systems, and, in particular, systems for ultrasonic tissue characterization, are derived. Most classifiers that are used today are dependent on a separation threshold, which can be chosen freely in many cases. The separation threshold separates the range of output values of the classification system into different target groups, thus conducting the actual classification process. In the first part of this paper, threshold specific performance measures, e.g., sensitivity and specificity, are presented. In the second part, a threshold-independent performance measure, the area under the ROC curve, is reviewed. Only the use of separation threshold-independent performance measures provides classification results that are overall representative for computer-aided diagnostic systems. The following text was motivated by the lack of a complete and definite discussion of the underlying subject in available textbooks, references and publications. Most manuscripts published so far address the theme of performance evaluation using ROC analysis in a manner too general to be practical for everyday use in the development of computer-aided diagnostic systems. Nowadays, the user of computer-aided diagnostic systems typically handles huge amounts of numerical data, not always distributed normally. Many assumptions made in more or less theoretical works on ROC analysis are no longer valid for real-life data. The paper aims at closing the gap between theoretical works and real-life data. The review provides the interested scientist with information needed to conduct ROC analysis and to integrate algorithms performing ROC analysis into classification systems while understanding the basic principles of classification.

Diagnosis, Computer-Assisted↗

PubMed2003

Ultrasonic multifeature tissue characterization for prostate diagnostics.

A new system for prostate diagnostics based on multifeature tissue characterization is proposed. Radiofrequency (RF) ultrasonic echo data are acquired during the standard transrectal ultrasound (US) imaging examination. Nine spectral, texture, first order and morphologic parameters are calculated and fed into two adaptive neuro-fuzzy inference systems (FIS) working in parallel. The outputs of the FISs are fed into a postprocessing procedure evaluating contextual information before being combined to form a malignancy map in which areas of high cancer probability are marked in red. The malignancy map is presented to the physician during the examination to improve the early detection of prostate cancer. The system has been evaluated on 100 patients undergoing radical prostatectomy. The ROC curve area using leave-one-out cross-validation over patients is A(Z) = 0.86 when distinguishing between hyperechoic and hypoechoic tumors and normal tissue and A(Z) = 0.84 when distinguishing between isoechoic tumors and healthy tissue, respectively. Tumors that are not visible in the conventional B-mode image can be located. Diagnosis of the prostate carcinoma using multifeature tissue characterization in combination with US imaging allows the detection of tumors at an early stage. Also, biopsy guidance and therapy planning can be improved.

Aged↗