Automatic Compartment Modelling and Segmentation for Dynamical Renal Scintigraphies

Daniel Ståhl, Kalle Åström, Niels Christian Overgaard, Matilda Landgren, Karl Sjöstrand, Lars Edenbrandt

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    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. In this paper 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.
    Original languageEnglish
    Title of host publicationImage Analysis : 17th Scandinavian Conference, SCIA 2011 - Ystad, Sweden, May 2011 - Proceedings
    Publication date2011
    Publication statusPublished - 2011
    Event17th Scandinavian Conference on Image Analysis (SCIA) - Ystad Saltsjöbad, Ystad, Sweden
    Duration: 23 May 201127 May 2011


    Conference17th Scandinavian Conference on Image Analysis (SCIA)
    LocationYstad Saltsjöbad
    Internet address
    SeriesLecture Notes in Computer Science


    • Dynamical renal scintigraphies
    • Compartment modelling
    • Time-resolved
    • Medical image analysis
    • Segmentation


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