Bjarne K Ersbøll | PubMed Health

PubMed2003

FAME--a flexible appearance modeling environment.

Combined modeling of pixel intensities and shape has proven to be a very robust and widely applicable approach to interpret images. As such the active appearance model (AAM) framework has been applied to a wide variety of problems within medical image analysis. This paper summarizes AAM applications within medicine and describes a public domain implementation, namely the flexible appearance modeling environment (FAME). We give guidelines for the use of this research platform, and show that the optimization techniques used renders it applicable to interactive medical applications. To increase performance and make models generalize better, we apply parallel analysis to obtain automatic and objective model truncation. Further, two different AAM training methods are compared along with a reference case study carried out on cross-sectional short-axis cardiac magnetic resonance images and face images. Source code and annotated data sets needed to reproduce the results are put in the public domain for further investigation.

Algorithms↗

PubMed2003

Diagnostic decision rule for support in clinical assessment of the need for surgical intervention in horses with acute abdominal pain.

A prospective survey of horses with colic referred to a university hospital was undertaken to elaborate on a simple clinical decision support system capable of predicting whether or not horses require surgical intervention. Cases were classified as requiring surgical intervention or not on the basis of intraoperative findings or necropsy reports. Logistic regression analysis was applied to identify predictors with the strongest association with treatment needed. The classification and regression tree (CART) methodology was used to combine the variables in a simple classification system. The performance of the elaborated algorithms, as diagnostic instruments, was recorded as test sensitivity and specificity. The CART method generated 5 different classification trees with a similar basic structure consisting of: degree of pain, peritoneal fluid colour, and rectal temperature. The tree, constructed at a prevalence of 15% surgical cases, appeared to be the best proposal made by CART. In this classification tree, further discrimination of cases was obtained by including the findings of rectal examination and packed cell volume. When regarded as a test system, the sensitivity and specificity was 52% and 95%, respectively, corresponding to positive and negative predictive values of 68% and 91%. The variables examined in the present study did not provide a safe clinical decision rule. The classification tree constructed at 15% surgical cases was considered feasible, the proportion of horses incorrectly predicted to be without need of immediate surgery (false negatives) was small, whereas the proportion of horses incorrectly predicted to be in need of immediate surgery (false positives) was large. Some of the false positive horses were amenable to surgical treatment, although these cases did not conform to the strict definition of a surgical case. A less rigorous definition of a surgical case than that used in the present study would lower the percentage of false positives.

Abdomen, Acute↗