# Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction

arXiv: https://arxiv.org/abs/2312.12990 · HTML: https://mlsanity.com/paper/2312.12990/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Deep Learning with Mixed Supervision for Brain Tumor Segmentation](https://mlsanity.com/paper/1812.04571/) (2018)

## Built on by (most cited first, 4 of 4)

- [CBCTLiTS: A Synthetic, Paired CBCT/CT Dataset For Segmentation And Style Transfer](https://mlsanity.com/paper/2407.14853/) (2024)
- [Enhancing Synthetic CT from CBCT via Multimodal Fusion: A Study on the Impact of CBCT Quality and Alignment](https://mlsanity.com/paper/2506.08716/) (2025)
- [Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation](https://mlsanity.com/paper/2406.11650/) (2024)
- [Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration](https://mlsanity.com/paper/2507.06067/) (2025)

## Abstract (arXiv)

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.