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.
@article{arxiv.2312.12990,
title = {Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction},
author = {Maximilian Ernst Tschuchnig and Julia Coste-Marin and Philipp Steininger and Michael Gadermayr},
journal= {arXiv preprint arXiv:2312.12990},
year = {2024}
}
Comments
Accepted and presented at German Conference on Medical Image Computing (BVM) 2024 edit: During work on this publication Maximilian Ernst Tschuchnig was affiliated with Salzburg University of Applied Sciences and University of Salzburg