Interactive Medical-SAM2 GUI: A Napari-based semi-automatic annotation tool for medical images
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911470000799744 |
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| author | Hong, Woojae Hwang, Jong Ha Chung, Jiyong Choi, Joongyeon Kim, Hyunngun Kim, Yong Hwy |
| author_facet | Hong, Woojae Hwang, Jong Ha Chung, Jiyong Choi, Joongyeon Kim, Hyunngun Kim, Yong Hwy |
| contents | Interactive Medical-SAM2 GUI is an open-source desktop application for semi-automatic annotation of 2D and 3D medical images. Built on the Napari multi-dimensional viewer, box/point prompting is integrated with SAM2-style propagation by treating a 3D volume as a slice sequence, enabling mask propagation from sparse prompts using Medical-SAM2 on top of SAM2. Voxel-level annotation remains essential for developing and validating medical imaging algorithms, yet manual labeling is slow and expensive for 3D scans, and existing integrations frequently emphasize per-slice interaction without providing a unified, cohort-oriented workflow for navigation, propagation, interactive correction, and quantitative export in a single local pipeline. To address this practical limitation, a local-first Napari workflow is provided for efficient 3D annotation across multiple studies using standard DICOM series and/or NIfTI volumes. Users can annotate cases sequentially under a single root folder with explicit proceed/skip actions, initialize objects via box-first prompting (including first/last-slice initialization for single-object propagation), refine predictions with point prompts, and finalize labels through prompt-first correction prior to saving. During export, per-object volumetry and 3D volume rendering are supported, and image geometry is preserved via SimpleITK. The GUI is implemented in Python using Napari and PyTorch, with optional N4 bias-field correction, and is intended exclusively for research annotation workflows. The code is released on the project page: https://github.com/SKKU-IBE/Medical-SAM2GUI/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22649 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Interactive Medical-SAM2 GUI: A Napari-based semi-automatic annotation tool for medical images Hong, Woojae Hwang, Jong Ha Chung, Jiyong Choi, Joongyeon Kim, Hyunngun Kim, Yong Hwy Computer Vision and Pattern Recognition Interactive Medical-SAM2 GUI is an open-source desktop application for semi-automatic annotation of 2D and 3D medical images. Built on the Napari multi-dimensional viewer, box/point prompting is integrated with SAM2-style propagation by treating a 3D volume as a slice sequence, enabling mask propagation from sparse prompts using Medical-SAM2 on top of SAM2. Voxel-level annotation remains essential for developing and validating medical imaging algorithms, yet manual labeling is slow and expensive for 3D scans, and existing integrations frequently emphasize per-slice interaction without providing a unified, cohort-oriented workflow for navigation, propagation, interactive correction, and quantitative export in a single local pipeline. To address this practical limitation, a local-first Napari workflow is provided for efficient 3D annotation across multiple studies using standard DICOM series and/or NIfTI volumes. Users can annotate cases sequentially under a single root folder with explicit proceed/skip actions, initialize objects via box-first prompting (including first/last-slice initialization for single-object propagation), refine predictions with point prompts, and finalize labels through prompt-first correction prior to saving. During export, per-object volumetry and 3D volume rendering are supported, and image geometry is preserved via SimpleITK. The GUI is implemented in Python using Napari and PyTorch, with optional N4 bias-field correction, and is intended exclusively for research annotation workflows. The code is released on the project page: https://github.com/SKKU-IBE/Medical-SAM2GUI/. |
| title | Interactive Medical-SAM2 GUI: A Napari-based semi-automatic annotation tool for medical images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.22649 |