When: 2011-05-04 09.00
Where: LTH, E-huset, E:2116
Speaker: Kalle Åström, Vision group, Matematikcentrum
In this talk I will present some recent results on mathematical modelling on renal scintigraphies. The talk is based on masters thesis work by Matilda Landgren on renal scintigraphies and Daniel Ståhl on time-resolved renal scintigraphies.
The field of image analysis has given medical doctors new tools for detecting and diagnosing various disorders in a more objective way. The images, that can be dicult to interpret if the experience is limited, are instead analysed by a software that e.g. gives indications of which regions that are of interest. An automated program is developed for analysing renal scintigraphy images of children. A segmentation of the kidneys is first performed using active shape models. The analysis of the 99mTc-DMSA uptake is done by comparing the uptake with the mean uptake in kidneys considered to have a normal uptake. Then a classication is done of the regions that deviates signicantly from the normal. The classier is tested on an independent group of 730 possible defects and the sensitivity of the classication is 96 % and the specicity is 85 %. This program would possibly aid interpreters in the detection and diagnosing of scars in the kidneys.
Time-resolved medical data has important applications in a large variety of medical applications. In this paper we study automatic analysis of dynamical renal scintigraphies. The traditional analysis pipeline for dynamical renal scintigraphies is to use manual or semiautomatic methods for segmentation of pixels into physical compartments, extract their corresponding time-activity curves and then compute the parameters that are relevant for medical assessment. We present a fully automatic system that incorporates spatial smoothing constraints, compartment modelling and positivity constraints to produce an interpretation of the full time-resolved data. The method has been tested on renal dynamical scintigraphies with promising results. It is shown that the method indeed produces more compact representations, while keeping the residual of fit low. The parameters of the time activity curve, such as peak-time and time for half activity from peak, are compared between the previous semiautomatic method and the method presented in this paper. It is also shown how to obtain new and clinically relevant features using our novel system.