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Automatic Compartment Modelling and Segmentation for Dynamical Renal Scintigraphies

Ståhl, Daniel LU ; Åström, Karl LU ; Overgaard, Niels Christian LU ; Landgren, Matilda LU ; Sjöstrand, Karl and Edenbrandt, Lars LU (2011) 17th Scandinavian Conference on Image Analysis (SCIA 2011) In Lecture Notes in Computer Science 6688. p.557-568
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Medical image analysis, time-resolved, compartment mod- elling, dynamical renal scintigraphies, segmentation
in
Lecture Notes in Computer Science
editor
Kahl, Fredrik; Heyden, Anders; and
volume
6688
pages
12 pages
publisher
Springer
conference name
17th Scandinavian Conference on Image Analysis (SCIA 2011)
external identifiers
  • wos:000308543900052
  • scopus:79957522975
ISSN
0302-9743
1611-3349
ISBN
978-3-642-21226-0 (print)
978-3-642-21227-7 (online)
DOI
10.1007/978-3-642-21227-7_52
language
English
LU publication?
yes
id
42de7248-d844-41c1-8c0a-42c071b9aa03 (old id 2214454)
date added to LUP
2011-12-29 13:31:03
date last changed
2017-08-27 03:45:00
@inproceedings{42de7248-d844-41c1-8c0a-42c071b9aa03,
  abstract     = {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.},
  author       = {Ståhl, Daniel and Åström, Karl and Overgaard, Niels Christian and Landgren, Matilda and Sjöstrand, Karl and Edenbrandt, Lars},
  booktitle    = {Lecture Notes in Computer Science},
  editor       = {Kahl, Fredrik and Heyden, Anders},
  isbn         = {978-3-642-21226-0 (print)},
  issn         = {0302-9743},
  keyword      = {Medical image analysis,time-resolved,compartment mod-
elling,dynamical renal scintigraphies,segmentation},
  language     = {eng},
  pages        = {557--568},
  publisher    = {Springer},
  title        = {Automatic Compartment Modelling and Segmentation for Dynamical Renal Scintigraphies},
  url          = {http://dx.doi.org/10.1007/978-3-642-21227-7_52},
  volume       = {6688},
  year         = {2011},
}