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the experiment on the first fold and compared the
results to those of our method for the same fold:
Although the blood-lumen segmentation is with
a DSC of 0.963 slightly better than our method,
yielding 0.951 for the same fold, the more complex
stent-graft segmentationdoesnotcomparewell,with
aDSCof0.785versusour method’s scoreof0.852.
7.Discussion
The strength of our method is the centerline-
guided segmentation method using the aortic center-
lines to optimize the patch locations during training
andinference. Whileourmethodyieldsbetter results
than a comparable model using a traditional sam-
pling setup, it also reduces the computational cost
significantly. For traditional patching using a grid
of overlapping tiles (when not allowing the patches
to contain regions outside the image) the number of
patchescalculatesas follows:
npatches= nd∏
d=1 d |I|d−θd|P|d−θd e (1)
wherend is the number of dimensions, |I|d the size
of an image, |P|d the size of a patch and θd the
overlap in dimension d. For our setup and a cho-
sen overlap of 32 voxels this results in 343 patches
on average per scan (|I| = (990,990,678), |P| =
(160,160,128), θd = (32,32,32)). Increasing
this overlap to improve the model’s performance
quickly raises this number, e.g., an overlap of half
the patch size (as used by Isensee et al. [10])
would result in 1440 patches on average per scan
(θd = (80,80,64)). Themajorityof thepatchesare
irrelevant for the result, as they do not intersect with
the target structure. By using the aorta centerline in-
formation, our method is able to greatly reduce the
number of patches, while also optimizing their con-
tent for training and inference. As a result, we can
target a smaller voxel spacing (which effectively re-
duces the physical extent of the patches) without the
disadvantages of excessive computational costs and
poormodelperformance.
Conclusions
We presented a novel centerline-guided method
for fullyautomatedsegmentationof theaorticblood-
lumen and the stent graft wire frame in abdominal
CT-Ascans. Usingourmethod,both trainingand in- ference can be conducted more efficiently. The eval-
uated DSC of 0.961 for the blood lumen and 0.841
for the stent graft wire frame suggest results that are
suitable for medical analysis. In the future, we plan
to use the results of our method for the analysis of
risk factors for post-EVAR patients. Futhermore, we
plantoextendtheuseofourmethodtoothermedical
segmentation tasks.
Acknowledgements
This work was funded by the FFG (Austrian Re-
search Promotion Agency) under the grants 851461
(EndoPredictor), 872604 (MEDUSA) and 867536
(vizARd). This project was supported by the strate-
gic economic and research programme ”Innovatives
OO¨ 2020” of the province of Upper Austria. RISC
Software GmbH is a Member of UAR (Upper Aus-
trianResearch) InnovationNetwork.
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106
Joint Austrian Computer Vision and Robotics Workshop 2020
- Titel
- Joint Austrian Computer Vision and Robotics Workshop 2020
- Herausgeber
- Graz University of Technology
- Ort
- Graz
- Datum
- 2020
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-752-6
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 188
- Kategorien
- Informatik
- Technik