Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction
This work addresses segmentation challenges in medical imaging for CBCT scans, offering incremental improvements through multi-task learning techniques.
The study tackled improving automated semantic segmentation of CBCT scans by using multi-task learning with a volume reconstruction task, finding that a patch-based approach enhanced segmentation in most cases and was further improved by a denoising method.
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.