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An Improved Method for Automatic Segmentation of the Left Ventricle in Myocardial Perfusion SPECT.

Fransson, Helen LU ; Ubachs, Joey LU ; Ugander, Martin LU ; Arheden, Håkan LU and Heiberg, Einar LU (2009) In Journal of Nuclear Medicine 50(2). p.205-213
Abstract
This study describes and validates a new method for automatic segmentation of left ventricular mass (LVM) in myocardial perfusion SPECT (MPS) images. This is important for estimating the size of a perfusion defect as percentage of the left ventricle. METHODS: A total of 101 patients with known or suspected coronary artery disease underwent both rest and stress MPS and MRI. A new automated algorithm was trained in 20 patients (40 MPS studies) and tested in 81 patients (162 MPS studies). The algorithm, which segmented the left ventricle in the MPS images, is based on Dijkstra's algorithm and finds an optimal mid-mural line through the left ventricular wall. From this line, the endocardium and epicardium are identified on the basis of an... (More)
This study describes and validates a new method for automatic segmentation of left ventricular mass (LVM) in myocardial perfusion SPECT (MPS) images. This is important for estimating the size of a perfusion defect as percentage of the left ventricle. METHODS: A total of 101 patients with known or suspected coronary artery disease underwent both rest and stress MPS and MRI. A new automated algorithm was trained in 20 patients (40 MPS studies) and tested in 81 patients (162 MPS studies). The algorithm, which segmented the left ventricle in the MPS images, is based on Dijkstra's algorithm and finds an optimal mid-mural line through the left ventricular wall. From this line, the endocardium and epicardium are identified on the basis of an individually estimated wall thickness and signal intensity. The algorithm was validated by comparing LVM in both stress and rest MPS, with LVM of the manually segmented left ventricle from MRI as the reference standard. For comparison, LVM was quantified using the software quantitative perfusion SPECT (QPS). RESULTS: The mean difference +/- SD in LVM between MPS and MRI was lower for the new method (6% +/- 15% LVM) than for QPS (18% +/- 19% LVM) for both mean difference (P < 0.001) and SD (P = 0.015). Linear regression analysis of LVM, comparing MPS and MRI, yielded R(2) = 0.83 using the new method and R(2) = 0.80 using QPS. Interstudy variability, measured as the coefficient of variance between rest MPS and stress MPS, was 6% for both the new method and QPS. Both the new algorithm and QPS systematically overestimated LVM in hearts with thin myocardium and underestimated LVM in hearts with thick myocardium. CONCLUSION: The new segmentation algorithm quantifies LVM with a significantly lower bias and variability than does the commercially available QPS software, when compared to manually segmented LVM by MRI. This makes the new algorithm an attractive method to use for estimating the size of the perfusion defect when expressing it as percentage of the left ventricle. This study shows that inaccurate estimation of wall thickness is the main source of error in automatic segmentation. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Nuclear Medicine
volume
50
issue
2
pages
205 - 213
publisher
Society of Nuclear Medicine
external identifiers
  • WOS:000263487800024
  • PMID:19164235
  • Scopus:59249102372
ISSN
0161-5505
DOI
10.2967/jnumed.108.057323
language
English
LU publication?
yes
id
67e59afa-9c66-4d2c-add1-fb06160f8373 (old id 1289344)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/19164235?dopt=Abstract
date added to LUP
2009-02-05 14:27:40
date last changed
2016-11-20 04:21:05
@misc{67e59afa-9c66-4d2c-add1-fb06160f8373,
  abstract     = {This study describes and validates a new method for automatic segmentation of left ventricular mass (LVM) in myocardial perfusion SPECT (MPS) images. This is important for estimating the size of a perfusion defect as percentage of the left ventricle. METHODS: A total of 101 patients with known or suspected coronary artery disease underwent both rest and stress MPS and MRI. A new automated algorithm was trained in 20 patients (40 MPS studies) and tested in 81 patients (162 MPS studies). The algorithm, which segmented the left ventricle in the MPS images, is based on Dijkstra's algorithm and finds an optimal mid-mural line through the left ventricular wall. From this line, the endocardium and epicardium are identified on the basis of an individually estimated wall thickness and signal intensity. The algorithm was validated by comparing LVM in both stress and rest MPS, with LVM of the manually segmented left ventricle from MRI as the reference standard. For comparison, LVM was quantified using the software quantitative perfusion SPECT (QPS). RESULTS: The mean difference +/- SD in LVM between MPS and MRI was lower for the new method (6% +/- 15% LVM) than for QPS (18% +/- 19% LVM) for both mean difference (P &lt; 0.001) and SD (P = 0.015). Linear regression analysis of LVM, comparing MPS and MRI, yielded R(2) = 0.83 using the new method and R(2) = 0.80 using QPS. Interstudy variability, measured as the coefficient of variance between rest MPS and stress MPS, was 6% for both the new method and QPS. Both the new algorithm and QPS systematically overestimated LVM in hearts with thin myocardium and underestimated LVM in hearts with thick myocardium. CONCLUSION: The new segmentation algorithm quantifies LVM with a significantly lower bias and variability than does the commercially available QPS software, when compared to manually segmented LVM by MRI. This makes the new algorithm an attractive method to use for estimating the size of the perfusion defect when expressing it as percentage of the left ventricle. This study shows that inaccurate estimation of wall thickness is the main source of error in automatic segmentation.},
  author       = {Fransson, Helen and Ubachs, Joey and Ugander, Martin and Arheden, Håkan and Heiberg, Einar},
  issn         = {0161-5505},
  language     = {eng},
  number       = {2},
  pages        = {205--213},
  publisher    = {ARRAY(0x979e3b8)},
  series       = {Journal of Nuclear Medicine},
  title        = {An Improved Method for Automatic Segmentation of the Left Ventricle in Myocardial Perfusion SPECT.},
  url          = {http://dx.doi.org/10.2967/jnumed.108.057323},
  volume       = {50},
  year         = {2009},
}