PyTK-SNAP: a Python-based Visualization Framework for Interactive Medical Image Analysis

Human interaction plays an important role in medical image analysis, involving tasks such as manual labeling, editing, comparison, and assessment of image data. In addition, modern foundational models often rely on user input to define tasks and generate predictions, underscoring the importance of human involvement. Interacting with medical images requires visualization support for efficient rendering and navigation of 3D images, which can be challenging and time-consuming to build from scratch. This work introduces a Python library designed to simplify the creation of interactive interfaces for medical image analysis. It offers visualization and basic interaction for volumetric images, binary masks, 3D meshes, and videos, with additional user-defined interactions well supported. The library allows for highly customizable interfaces without limitations on view layouts or the number of images displayed simultaneously, unlike existing tools like 3DSlicer, ITK-SNAP, and Napari. Additionally, its Python-based design ensures compatibility with most deep-learning methods, reducing development and operational costs compared to heterogeneous visualization solutions. We demonstrate low interactive latency with our library and showcase its versatility through six interactive applications covering diverse practical scenarios. Our code is open-sourced at https://github.com/MIP-Lab/pytk-snap.git.