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.

翻译：语义分割是医学图像处理中的关键任务，对于分割器官或肿瘤等病变区域至关重要。本研究旨在通过多任务学习提升CBCT图像中的自动分割性能。为评估对不同体积质量的影响，我们从CT肝脏肿瘤分割基准（LiTS）数据集中合成了CBCT数据集。我们探索了两种改善分割的方法：其一，通过多任务学习引入基于形态学正则化的体积重建任务；其二，利用此重建任务重构质量最佳的CBCT（最接近原始CT质量），从而产生去噪效果。我们分别研究了整体与分块两种方法。实验结果表明，尤其在采用分块方法时，多任务学习在多数情况下能提升分割效果，且通过去噪方法可进一步优化这些结果。