Male and female survival under three artificial light sources: 1) light-emitting diode (LED, green); 2) fluorescent lamp (FL, blue); and 3) halogen lamp (HL, amber) during the 15 days for Experiment 1 [a], Experiment 2 [b], and Experiment 3 [c].

<p>Male and female survival under three artificial light sources: 1) light-emitting diode (LED, green); 2) fluorescent lamp (FL, blue); and 3) halogen lamp (HL, amber) during the 15 days for Experiment 1 [a], Experiment 2 [b], and Experiment 3 [c].</p

Light spectra (μmol m^-2 s^-1) of three artificial light sources: 1) light-emitting diode (LED, green); 2) fluorescent lamp (FL, blue); and 3) halogen lamp (HL, amber) at wavelengths from 300 to 885 nm.

<p>Light spectra (μmol m^-2 s^-1) of three artificial light sources: 1) light-emitting diode (LED, green); 2) fluorescent lamp (FL, blue); and 3) halogen lamp (HL, amber) at wavelengths from 300 to 885 nm.</p

Top view of the position of the three light sources: 1) light-emitting diode (LED, green); 2) fluorescent lamp (FL, blue); and 3) halogen lamp (HL, amber); the contours of polyethylene cages are drawn in black.

<p>Top view of the position of the three light sources: 1) light-emitting diode (LED, green); 2) fluorescent lamp (FL, blue); and 3) halogen lamp (HL, amber); the contours of polyethylene cages are drawn in black.</p

Value distribution of the masked image stacks (within the brain area) shown in Fig 2 within the created dataset.

Value distribution of the masked image stacks (within the brain area) shown in Fig 2 within the created dataset.</p

S1 Appendix -

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.</div

Distribution of foreground information (vessels) and background information within the image stacks of the created MRI dataset.

Distribution of foreground information (vessels) and background information within the image stacks of the created MRI dataset.</p

The pre-processing steps, that are applied to the individual image stacks as basis for the training of the segmentation model.

The pre-processing steps, that are applied to the individual image stacks as basis for the training of the segmentation model.</p

Fig 2 -

The eight mice image stacks of the dataset showing the wild type mice in (a) to (d) and the knockout types in (e) to (h), each represented by one masked layer (l = 200) and the associated histogram. The first two images per row show brains of adult (a) (b) (e) (f) and the remaining two of young mice (c) (d) (g) (h). The histogram shows the percentage value distribution within the brain area of the whole stack.</p

Average performance measurements and the associated standard deviation of the segmentation methods applied to the eight image stacks using the proposed U-Net model and the state-of-the-art Vesselness filter, as well as for the used post-processing methods.

Average performance measurements and the associated standard deviation of the segmentation methods applied to the eight image stacks using the proposed U-Net model and the state-of-the-art Vesselness filter, as well as for the used post-processing methods.</p

The two evaluated post-processing methodologies, namely the threshold and the region growing approaches, which are applied after the model prediction using the trained U-Net model to get the actual predicted vessels.

The two evaluated post-processing methodologies, namely the threshold and the region growing approaches, which are applied after the model prediction using the trained U-Net model to get the actual predicted vessels.</p