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low-resolution data blood-lumen mask
annotated dataset centerline information
blood-lumen and
stent-graft
patcheshigh-resolution
data high-resolution
segmentation
train
train merge
extract
patch locations
M1
M2
Figure 2. Outline of our method: The top branch shows the centerline extraction step (modelM1) and the bottom branch
thepatchwise segmentationstep (modelM2).
challenges arise due to considerable imaging arti-
facts caused by the stent-graft wire frame and the
distinctboundariesbetweenblood lumenand throm-
bus. While there are a number of publications on the
segmentation of the abdominal aorta, very few have
focusedonstent segmentation. Kleinetal. [12]used
agraph-basedmethodtocreateageometricmodelof
thestent-graft, disregarding theaortaentirely. To the
bestofourknowledge, there isnot a singleapproach
segmenting both structures simultaneously. For the
segmentation of the abdominal aorta, traditional ap-
proaches includegraph-basedmethods [6,4,23]and
deformable-models [13, 14] which require user in-
teraction to varying degrees and have predominantly
been evaluated on pre-operative scans. A common
problem with graph- and deformable-model-based
approaches is the introduction of many parameters
optimized for the respective dataset, limiting the ro-
bustness and applicability of the methods in clinical
settings [17]. With the introduction of the convolu-
tionalneuralnetwork(CNN)thefieldofmedical im-
age analysis changed significantly. Today the U-Net
[21] and its 3D equivalent [8] are the most widely
models used for medical image segmentation. Both
models have been applied to the task of the abdom-
inal aorta segmentation, Zheng et al. [26] reporting
a Dice similarity coefficient (DSC) of 0.82 for the
aneurysm thrombus and Li et al. [16] reporting a
DSC of0.92 for the aorta blood lumen. For the seg- mentation of blood lumen and stent graft wire frame
we will therefore likewise rely on the (3D) U-Net
architecture. The distinguishing challenge to other
segmentation tasks is inourcase thefinestructureof
the stent-graft, with a diameter as small as 0.4mm
[24],whichrequiresanexceptionallyhighresolution
for accurate reconstruction, pushing the limitations
ofmodernhardware.
3.Dataset
Our dataset consists of 76 abdominal CTA scans
of 36 patients treated with EVAR that we received
fromtheKeplerUniversityHospitalLinz. Eachscan
consists of 155 to 873 axial slices with 512× 512
voxels. There are large differences in the resolu-
tion with a minimum voxel spacing ranging from
0.404mm frontal/sagittal and 0.8mm longitudinal
(a) (b)
Figure 3. Examples of two ground truth segmentations:
Medtronic Endurant (a) and Anaconda (b). In total the
dataset contains5 different types of stent-grafts.
103
Joint Austrian Computer Vision and Robotics Workshop 2020
- Title
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Editor
- Graz University of Technology
- Location
- Graz
- Date
- 2020
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Size
- 21.0 x 29.7 cm
- Pages
- 188
- Categories
- Informatik
- Technik