Interactive Medical-SAM2 GUI: A Napari-based semi-automatic annotation tool for medical images

TL;DR

Medical-SAM2 GUI is an open-source, Napari-based tool that streamlines semi-automatic annotation of 2D and 3D medical images, integrating propagation, correction, and export in a unified workflow.

Contribution

It introduces a comprehensive, local-first annotation pipeline for medical images that combines SAM2-style propagation with interactive correction and cohort management.

Findings

Enables efficient annotation of large 3D medical datasets.

Supports accurate mask propagation and correction workflows.

Provides export features including volumetry and 3D rendering.

Abstract

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/.