Top-K Maximum Intensity Projection Priors for 3D Liver Vessel Segmentation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915183094398976 |
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| author | Zhang, Xiaotong Broersen, Alexander van Erp, Gonnie CM Pintea, Silvia L. Dijkstra, Jouke |
| author_facet | Zhang, Xiaotong Broersen, Alexander van Erp, Gonnie CM Pintea, Silvia L. Dijkstra, Jouke |
| contents | Liver-vessel segmentation is an essential task in the pre-operative planning of liver resection. State-of-the-art 2D or 3D convolution-based methods focusing on liver vessel segmentation on 2D CT cross-sectional views, which do not take into account the global liver-vessel topology. To maintain this global vessel topology, we rely on the underlying physics used in the CT reconstruction process, and apply this to liver-vessel segmentation. Concretely, we introduce the concept of top-k maximum intensity projections, which mimics the CT reconstruction by replacing the integral along each projection direction, with keeping the top-k maxima along each projection direction. We use these top-k maximum projections to condition a diffusion model and generate 3D liver-vessel trees. We evaluate our 3D liver-vessel segmentation on the 3D-ircadb-01 dataset, and achieve the highest Dice coefficient, intersection-over-union (IoU), and Sensitivity scores compared to prior work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Top-K Maximum Intensity Projection Priors for 3D Liver Vessel Segmentation Zhang, Xiaotong Broersen, Alexander van Erp, Gonnie CM Pintea, Silvia L. Dijkstra, Jouke Computer Vision and Pattern Recognition Liver-vessel segmentation is an essential task in the pre-operative planning of liver resection. State-of-the-art 2D or 3D convolution-based methods focusing on liver vessel segmentation on 2D CT cross-sectional views, which do not take into account the global liver-vessel topology. To maintain this global vessel topology, we rely on the underlying physics used in the CT reconstruction process, and apply this to liver-vessel segmentation. Concretely, we introduce the concept of top-k maximum intensity projections, which mimics the CT reconstruction by replacing the integral along each projection direction, with keeping the top-k maxima along each projection direction. We use these top-k maximum projections to condition a diffusion model and generate 3D liver-vessel trees. We evaluate our 3D liver-vessel segmentation on the 3D-ircadb-01 dataset, and achieve the highest Dice coefficient, intersection-over-union (IoU), and Sensitivity scores compared to prior work. |
| title | Top-K Maximum Intensity Projection Priors for 3D Liver Vessel Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.03367 |