TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Praschl, Christoph
A1  - Zopf, Lydia M.
A1  - Kiemeyer, Emma
A1  - Langthallner, Ines
A1  - Ritzberger, Daniel
A1  - Slowak, Adrian
A1  - Weigl, Martin
A1  - Blüml, Valentin
A1  - Nešić, Nebojša
A1  - Stojmenović, Miloš
A1  - Kniewallner, Kathrin M.
A1  - Aigner, Ludwig
A1  - Winkler, Stephan
A1  - Walter, Andreas
T1  - U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets
JF  - PLOS ONE
N2  - Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer’s. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper—quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:944-opus4-32412
U6  - https://doi.org/10.1371/journal.pone.0291946
DO  - https://doi.org/10.1371/journal.pone.0291946
VL  - 18
IS  - 10
SP  - 19
S1  - 19
ER  -