Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using
  Image Reconstruction
Maximilian Ernst Tschuchnig Julia Coste-Marin Philipp Steininger Michael Gadermayr
Semantic segmentation is a crucial task in medical image processing,
essential for segmenting organs or lesions such as tumors. In this study we aim
to improve automated segmentation in CBCTs through multi-task learning. To
evaluate effects on different volume qualities, a CBCT dataset is synthesised
from the CT Liver Tumor Segmentation Benchmark (LiTS) dataset. To improve
segmentation, two approaches are investigated. First, we perform multi-task
learning to add morphology based regularization through a volume reconstruction
task. Second, we use this reconstruction task to reconstruct the best quality
CBCT (most similar to the original CT), facilitating denoising effects. We
explore both holistic and patch-based approaches. Our findings reveal that,
especially using a patch-based approach, multi-task learning improves
segmentation in most cases and that these results can further be improved by
our denoising approach.