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

• URL: http://arxiv.org/abs/2312.12990v1
• Date: Wed, 20 Dec 2023 12:48:18 GMT
• Title: Multi-task Learning To Improve Semantic Segmentation Of CBCT Scans Using Image Reconstruction
• Authors: Maximilian Ernst Tschuchnig, Julia Coste-Marin, Philipp Steininger, Michael Gadermayr
• Abstract summary: We aim to improve automated segmentation in CBCTs through multi-task learning. To improve segmentation, two approaches are investigated. First, we perform multi-task learning to add morphology based regularization. Second, we use this reconstruction task to reconstruct the best quality CBCT.
• Score: 0.8739101659113155
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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