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

Source: [Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction](<https://www.sota2.com/research/paper/68fa75f71e209131cea6ab09>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:11.539Z

Authors: Maximilian Ernst Tschuchnig, Julia Coste-Marin, Philipp Steininger, Michael Gadermayr

Published: 2023-12-20T12:48:18.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2312.12990>)

[PDF](<https://arxiv.org/pdf/2312.12990.pdf>)