=Paper=
{{Paper
|id=None
|storemode=property
|title=Semi-Automatic 4D Fuzzy Connectedness Segmentation of Heart Ventricles in Cine MRI
|pdfUrl=https://ceur-ws.org/Vol-715/bvm2011_3.pdf
|volume=Vol-715
}}
==Semi-Automatic 4D Fuzzy Connectedness Segmentation of Heart Ventricles in Cine MRI==
https://ceur-ws.org/Vol-715/bvm2011_3.pdf
Semi-Automatic 4D Fuzzy Connectedness
Segmentation of Heart Ventricles in Cine MRI
Markus Hüllebrand1 , Anja Hennemuth1 ,
Daniel Messroghli2 ,Titus Kühne2 ,Ola Friman1
1
Fraunhofer MEVIS - Institute for Medical Image Computing, Bremen, Germany
2
German Heart Institute Berlin, Berlin, Germany
markus.huellebrand@mevis.fraunhofer.de
Abstract. The volumes of the left and right cardiac ventricles are im-
portant indexes for several cardiovascular diseases. In this paper, a semi-
automatic algorithm based on 4D fuzzy connectedness is proposed for
segmenting the cardiac ventricles in arbitrarily oriented cardiac cine MR
images. The segmentation of the left ventricle was evaluated using 45
short axis images and a visual assessment of right ventricle segmentation
was performed.
1 Introduction
Cardiovascular diseases are the most common causes of death in western nations.
Important diagnostic parameters for characterizing heart function are the vol-
umes of the left ventricle (LV) and right ventricle (RV) at the end systolic (ESV)
and end diastolic (EDV) time points. Parameters derived from these quantities
such as stroke volume (SV= EDV-ESV) and ejection fraction (EF = SV/EDF)
are also commonly reported. The LV and RV can be segmented and quantified
in cardiac CT or MR images. As these images are 3D or 3D+time, manual
segmentation is very time consuming and suffers from inter- and intra-observer
variability. Therefore, over the last years, several (semi-) automatic approaches
have been proposed for segmenting the LV and RV. A particular focus has been
on the segmentation of the LV, which is larger and stronger than the RV, and
also more easily segmented due to its regular conic shape. Strongly model-based
active shape approaches have therefore been devised for the LV, see for example
the MICCAI 2009 LV Segmentation Challenge1 . In contrast, the RV is more
difficult to segment due to a more complex anatomy and weaker image contrast.
Moreover, model-based approaches are often optimized for one image orienta-
tion, and they experience difficulties for patients with severe heart diseases and
with non-standard anatomy, e.g., in congenital heart disease. In this work, a
semi-automatic 4D fuzzy connectedness algorithm is proposed for a 4D segmen-
tation of the LV and RV. No specific shape information is assumed and the
method is therefore not restricted by the image view or pathologic anatomies.
1
http://smial.sri.utoronto.ca/LV Challenge/Home.html
4 Hüllebrand et al.
There are two main contributions of this paper: First, the 3D fuzzy connect-
edness algorithm in [1] has been extended to 4D. Second, an image processing
pipeline based on the 4D fuzzy connectedness for segmenting the LV and RV
from cardiac cine MRI images has been developed. Fuzzy connectedness has
previously been proposed for heart ventricle segmentation [2, 3, 4], but only for
the LV and 3D.
2 Materials and Methods
2.1 4D fuzzy connectedness
Udupa and Samarasekera [5] introduced the fuzzy connectedness framework in
which the degree of connectedness between two voxels u and v is defined by
considering all possible paths between u and v. The cost S [ρ(u, v)] of a path
ρ(u, v) is defined as the minimal image intensity value along the path. The fuzzy
connectedness C(u, v) is now defined by the path with the maximum cost, i.e.,
C(u, v) = max S [ρ(u, v)] (1)
ρ(u,v)
Seeing the image as a height map, the fuzzy connectedness is defined by the
path between u and v along which the lowest height is as large as possible, and
the connectedness is this minimum height. Tizon and Smedby [1] proposed a
fast 3D implementation of the fuzzy connectedness technique using a forward-
back scan. In this work, this algorithm was extended to handle up to 6D im-
ages and it was implemented in C++ in the free software platform MeVisLab
(www.mevislab.de). The forward scan mask for 4D fuzzy connectedness is shown
in Fig. 1. The fuzzy connectedness framework can be used for image segmenta-
tion by calculating the degree of connectivity for each voxel in the image to a
set of seed voxels of different classes or labels. Each voxel is classified according
to which seed point it has the largest connectivity.
2.2 Segmentation of the left and right ventricle
The classic segmentation approach using the fuzzy connectedness is to calculate
the connectedness map and apply a threshold to it. The goal here is provide
Fig. 1. Neighbourhood masks at time- A B
point t-1 (A) and t (B).
Segmentation of Heart Ventricles in Cine MRI 5
Fig. 2. Bland altman plot comparing the
manual and semi-automatic measurement of
the ejection fraction.
a spatio-temporal segmentation of the MRI image into three classes: LV, RV
and background. To this end, a combination of the classic fuzzy connectedness
approach combined with an initial separation of the LV and RV by applying the
fuzzy connectedness algorithm to a derivative-of-Gaussian gradient magnitude
image is proposed. Manual input is required in form of seed areas in the LV and
RV for one timepoint, and the definition of a valve plane to stop the ventricle
segmentation from leaking into the aorta. The ventricles are then considered as
a 4D object spatio-temporal objects to be segmented. The fuzzy connectedness
algorithm also requires seed markers for the background class, which are straight-
forwardly derived automatically from the image histogram. In the initial step,
the 4D fuzzy connectedness algorithm is applied on a gradient magnitude image,
classifying each voxel according to which marker class (LV, RV or background)
it has the greatest affinity.
This initial segmentation tends to be under-segmented at the myocardial
borders, caused by the application to the a smoothed gradient image. Leak-
age around the RV can also be observed. Therefore, a second traditional fuzzy
connectedness step is applied twice to the original image and for LV and RV
separately, i.e., into {LV,background} and {RV,background} respectively. Fur-
thermore, the fuzzy connectedness is only applied to an area of interest which is
found by dilating the initial segmentations of the LV and RV by 5 voxels. The
thresholds for the connectedness maps are found by an automatic analysis of the
grey value distribution inside the initially segmented volumes of the LV and RV,
providing the final spatio-temporal segmentation. The papillary muscles will not
be included in the segmentation due to their deviating image contrast, but they
can be included by calculation the convex hull of the segmentation masks.
Fig. 3. The images show the delineation of the
left(dashed) and right(dotted) ventricular blood pool.
In upper row the results in three slices at end dias-
tole, in the lower row three slices at end systole are
visualised. The red arrows indicate that the papillary
muscles are excluded.
6 Hüllebrand et al.
2.3 Data
Cine steady state free precession (SSFP) MR short axis images of 45 subjects
from the MICCAI 2009 LV Segmentation Challenge were used for evaluating
the LV segmentation. The 4D images have an in-plane resolution of 256×256
voxels, 6-12 slices in the z-dimension, and 20 time points over the heart cycle.
A manual delineation of the endocardium at the end-systole and end-diastole
phases, including the papillary muscles, is available. In addition, images in
short axis and transversal orientations from the Deutsches Herzzentrum Berlin
were used for visual assessment of both LV and RV.
3 Results
As proposed in the MICCAI Challenge 2009, a Bland-Altman analysis is used to
compare the algorithmic results with the manual segmentation, see (Fig. 2) for a
Bland-Altman plot for the ejection fraction (EF). The mean difference observed
between of the manual and semi-automatic segmentation for the EF is 1.2±5.6%
and for the volume 13.1 ± 23.3 ml. The best values from the MICCAI Challenge
are 1.7 ± 8.5 % and 22.5 ± 26.2 ml respectively. Furthermore, the segmentation
masks have been compared using the Dice coefficient and the symmetric average
perpendicular distance. The mean Dice coefficient is 0.84 ± 0.07 and the mean
average perpendicular distance (APD) is 2.96 ± 1.09 mm. As reference, one of
the best methods of the MICCAI challenge achieved a mean Dice of 0.89 ± 0.03
and a mean APD of 2.29 ± 0.57 mm. Finally, results of the segmentation of LV
and RV in the data from the Deutsches Herzzentrum Berlin are shown in Fig. 3
and Fig. 2.2. Figure 2.2c shows the volumes of the LV (red) and RV (blue) over
one heart beat. The interactive segmentation time of one datataset is about 2
to 5 minutes, and the calculation time of the 4D fuzzy connectedness algorithm
is about 5 seconds.
Fig. 4. A and B show the volume rendering of a segmentation result of left and right
ventricle corresponding to Fig. 3 at end-diastole(A) and end-systole(B). C visualises the
volume curve for each ventricle over one cardiac cycle, where the red curve corresponds
to the left and the blue to the right ventricle.
Segmentation of Heart Ventricles in Cine MRI 7
Fig. 5. Dashed (inner): Segmentation result FC.
Marked: Convex hull of segmentation result. Solid
(outer): Expert contour for myocardial thickness.
4 Discussion
The results of the evaluation indicate that the proposed approach provides sim-
ilar accuracy as a manual segmentation of the left ventricle. Comparing our
results with those of 2009 MICCAI Challenge, one can see that the results of
the best participants have slightly higher scores regarding the Dice coefficient
and the average perpendicular distance, whereas our method is superior in terms
of the clinically more relevant EF. Here it should be stressed that the manual
segmentations provided by the experts are intended for measurement of myocar-
dial thickness, whereas the proposed method aims to delineate the blood pool
(Fig. 5), which may explain this result. Moreover, the proposed algorithm is
principally not restricted to specific image orientation, e.g., short axis images,
as it works fully 4D. This is in contrast to many model-based algorithms which
often work with 2D contours. In future work the RV segmentation must be
evaluated more thoroughly. It was noted in this work that segmentation leakage
was a larger problem for the RV than the LV, and that more background seed
points must sometimes be set for the fuzzy connectedness algorithm to obtain
an accurate result. Further work on placing and tracking the valve plane is also
planned.
References
1. Tizon X, Smedby O. Segmentation with gray-scale connectedness can separate
arteries and veins in MRA. J Magn Reson Imaging. 2002;15(4):438–45.
2. Pednekar A, Kurkure U, Muthupillai R, et al. Left ventricular segmentation in MR
using hierarchical multi-class multi-feature fuzzy connectedness. Lect Notes Comput
Sci. 2004;3216:402–10.
3. Fleureau J, Garreau M, Simon A, et al. Assessment of global cardiac function in
MSCT imaging using fuzzy connectedness segmentation. In: Comput Cardiol; 2008.
p. 725–8.
4. Pednekar AS, Muthupillai R, Cheong B, et al. Automatic computation of left ven-
tricular ejection fraction from spatiotemporal information in cine-SSFP cardiac MR
images. J Magn Reson Imaging. 2008;28(1):39–50.
5. Udupa JK, Samarasekera S. Fuzzy connectedness and object definition: theory,
algorithms, and applications in image segmentation. Graph Models Image Process.
1996;58(3):246–61.