autoPET IV challenge: Incorporating organ supervision and human guidance for lesion segmentation in PET/CT
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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_ | 1866918134421651456 |
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| author | Huang, Junwei Hao, Yingqi Luo, Yitong Wang, Ziyu Liu, Mingxuan Chen, Yifei Wang, Yuanhan Xiang, Lei Tian, Qiyuan |
| author_facet | Huang, Junwei Hao, Yingqi Luo, Yitong Wang, Ziyu Liu, Mingxuan Chen, Yifei Wang, Yuanhan Xiang, Lei Tian, Qiyuan |
| contents | Lesion Segmentation in PET/CT scans is an essential part of modern oncological workflows. To address the challenges of time-intensive manual annotation and high inter-observer variability, the autoPET challenge series seeks to advance automated segmentation methods in complex multi-tracer and multi-center settings. Building on this foundation, autoPET IV introduces a human-in-the-loop scenario to efficiently utilize interactive human guidance in segmentation tasks. In this work, we incorporated tracer classification, organ supervision and simulated clicks guidance into the nnUNet Residual Encoder framework, forming an integrated pipeline that demonstrates robust performance in a fully automated (zero-guidance) context and efficiently leverages iterative interactions to progressively enhance segmentation accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | autoPET IV challenge: Incorporating organ supervision and human guidance for lesion segmentation in PET/CT Huang, Junwei Hao, Yingqi Luo, Yitong Wang, Ziyu Liu, Mingxuan Chen, Yifei Wang, Yuanhan Xiang, Lei Tian, Qiyuan Image and Video Processing Lesion Segmentation in PET/CT scans is an essential part of modern oncological workflows. To address the challenges of time-intensive manual annotation and high inter-observer variability, the autoPET challenge series seeks to advance automated segmentation methods in complex multi-tracer and multi-center settings. Building on this foundation, autoPET IV introduces a human-in-the-loop scenario to efficiently utilize interactive human guidance in segmentation tasks. In this work, we incorporated tracer classification, organ supervision and simulated clicks guidance into the nnUNet Residual Encoder framework, forming an integrated pipeline that demonstrates robust performance in a fully automated (zero-guidance) context and efficiently leverages iterative interactions to progressively enhance segmentation accuracy. |
| title | autoPET IV challenge: Incorporating organ supervision and human guidance for lesion segmentation in PET/CT |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2509.02402 |