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Automatic Compartment Modelling and Segmentation for Dynamical Renal Scintigraphies

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Author

Editor

  • Fredrik Kahl
  • Anders Heyden

Summary, in English

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.

Publishing year

2011

Language

English

Pages

557-568

Publication/Series

Lecture Notes in Computer Science

Volume

6688

Document type

Conference paper

Publisher

Springer

Topic

  • Computer Vision and Robotics (Autonomous Systems)
  • Mathematics

Keywords

  • Medical image analysis
  • time-resolved
  • compartment mod-elling
  • dynamical renal scintigraphies
  • segmentation

Conference name

17th Scandinavian Conference on Image Analysis (SCIA 2011)

Conference date

2011-05-23 - 2011-05-27

Conference place

Ystad, Sweden

Status

Published

Research group

  • Nuclear medicine, Malmö
  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISSN: 0302-9743
  • ISSN: 1611-3349
  • ISBN: 978-3-642-21226-0 (print)
  • ISBN: 978-3-642-21227-7 (online)