@misc{9248511,
  abstract     = {{The last decades have seen great improvement in the treatment of children with brain tumours,
5-year survival has increased from 20% in 1950 to 80% in 2020. Current treatment can consist
of a combination of surgery, chemotherapy and radiation therapy (RT). This improved chance
of survival has placed increased focus on the quality of life of the survivors post treatment.
RT increases the risk of neurocognitive complications, and the risk and complications depend
on the radiation dose to organs at risk (OAR) in the brain. Today, the OARs are manually
segmented for RT planning, which is time-consuming, since no commercially available solution
exists for automatic segmentation of this patient group. The aim of this project is to create a
deep learning (DL) model that performs automatic segmentation of brain OARs in paediatric
patients.

A model was created based on a U-Net architecture using the framework no new U-Net (nnU-
net). The network takes a 3D image as an input and output segmentation masks of the OARs it
was trained on. nnU-Net was adapted to include residual connections on both the encoding and
decoding paths and CBAM attention modules on the encoding path. Tversky loss was added
to the compound loss function to improve performance on small OARs.

The training data consisted of 40 3D images, acquired using magnetic resonance imaging (MRI),
with manual segmentations, and 5 additional patients were placed in a test set to evaluate the
final model. The manual segmentations consisted of hypothalamus, thalamus, hippocampus,
brainstem, cerebellum, temporal- and frontal lobes. 5-fold cross-validation splits during training
were stratified based on age to reduce the anatomical effects of age. The final model was
evaluated with Dice’s similarity coefficient (DSC) and the 95th-percentile Hausdorff distance
(dH95) against the ground truth segmentations as well as a review by a physician to grade the
segmentations.
For cerebellum and brainstem, the model achieved median DSC and dH95 of 0.97 [0.84 - 0.97]
and 1.1 [1.0 - 5.0] mm, and 0.93 [0.79 - 0.94] and 2.0 [1.6 - 5.5] mm, respectively. The frontal
and temporal lobes showed median DSC and dH95 of 0.96 [0.88 - 0.96] and 2.0 [1.9 - 5.0] mm,
and 0.90 [0.71 - 0.92] and 4.4 [3.1 - 10.0] mm, respectively. For hippocampus, thalamus and
hypothalamus, the results were 0.86 [0.66 - 0.88] and 1.4 [1.1 - 5.0] mm, 0.86 [0.78 - 0.93] and
2.2 [1.6 - 4.0] mm, and 0.72 [0.50 - 0.74] and 2.2 [1.7 - 5.2] mm. The results from the physician
grading show almost identical overall scoring between the model and manual segmentation. The
model was graded worse on the frontal lobe as compared with the manual segmentation but
better on hippocampus.

Overall results show high DSC and low dH95 for all OARs, only hypothalamus having a no-
ticeably lower DSC and the temporal lobe having a higher dH95. nnU-Net provided a solid
foundation for the network and together with the modifications resulted in a strong architec-
ture. Evaluating the test patients, one outlier with considerably lower performance compared
to the other patients was observed. This patient had lower MRI image quality, indicating image
quality affect model performance. This is also reflected in the physician review as segmentations
of the lower quality image was graded lower with higher self-reported uncertainty. Setting the
caudal limit too high was the most stated flaw in the predicted segmentations, especially in the
frontal lobe. However, for the same patient both segmentations received almost equal grading.
The presented DL model shows great promise of reducing the workload for segmenting OAR
in paediatric patients with brain tumours, aiding in clinical treatment planning and improving
research possibilities.}},
  author       = {{Ewald, Erik}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Deep learning based automatic segmentation of organs at risk in children with brain tumours}},
  year         = {{2026}},
}