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)
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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2214454
- author
- Ståhl, Daniel
LU
; Åström, Karl
LU
; Overgaard, Niels Christian
LU
; Landgren, Matilda
LU
; Sjöstrand, Karl
and Edenbrandt, Lars
LU
- organization
- publishing date
- 2011
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Medical image analysis, time-resolved, compartment mod-elling, dynamical renal scintigraphies, segmentation
- host publication
- Lecture Notes in Computer Science
- editor
- Kahl, Fredrik and Heyden, Anders
- volume
- 6688
- pages
- 12 pages
- publisher
- Springer
- conference name
- 17th Scandinavian Conference on Image Analysis (SCIA 2011)
- conference location
- Ystad, Sweden
- conference dates
- 2011-05-23 - 2011-05-27
- external identifiers
-
- wos:000308543900052
- scopus:79957522975
- ISSN
- 1611-3349
- 0302-9743
- 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
- 2016-04-01 11:13:58
- date last changed
- 2024-01-07 10:52:42
@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 = {{1611-3349}},
keywords = {{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}},
doi = {{10.1007/978-3-642-21227-7_52}},
volume = {{6688}},
year = {{2011}},
}