tarmsegmenter

tarmsegmenter is an open-source software library and command-line tool designed for image segmentation. It is intended to be adaptable across domains such as medical imaging, computer vision research, and industrial inspection, providing a modular framework that combines traditional segmentation methods with modern deep learning approaches to produce accurate region delineations.

The project offers support for 2D and 3D data and enables multiple segmentation paradigms, including semantic, instance, and panoptic segmentation. It can operate on CPU or GPU hardware and includes data loaders for common formats such as NIfTI, DICOM, TIFF, and PNG. Typical workflows include preprocessing, model inference, and post-processing. Users can employ pre-trained models or train and fine-tune models on their own datasets. A Python API and a command-line interface are provided to facilitate scripting and integration into larger data pipelines. Evaluation utilities are available to compute metrics such as the dice coefficient and Intersection over Union, alongside visualization helpers.

Implementation-wise, tarmsegmenter is primarily written in Python with performance-critical components potentially exposed as compiled extensions. It follows a modular plugin architecture that allows new models, loss functions, and post-processing steps to be added without altering core code. The project emphasizes reproducibility through configuration files and experiment-tracking capabilities, aiding researchers and developers in benchmarking and collaboration.

Development and availability are centered on community-driven open-source practices, with ongoing maintenance, documentation, and tutorials published for installation and usage. Users should verify compatibility with their hardware, data formats, and licensing terms before deployment, particularly in commercial settings. See also general image segmentation and medical image analysis resources for related topics.