Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917853155819520 |
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| author | Tschuchnig, Maximilian Ernst Coste-Marin, Julia Steininger, Philipp Gadermayr, Michael |
| author_facet | Tschuchnig, Maximilian Ernst Coste-Marin, Julia Steininger, Philipp Gadermayr, Michael |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_12990 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction Tschuchnig, Maximilian Ernst Coste-Marin, Julia Steininger, Philipp Gadermayr, Michael Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.12990 |