# Seg-metrics: a Python package to compute segmentation metrics

arXiv: https://arxiv.org/abs/2403.07884 · HTML: https://mlsanity.com/paper/2403.07884/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)

## Built on by (most cited first, 4 of 4)

- [Understanding implementation pitfalls of distance-based metrics for image segmentation](https://mlsanity.com/paper/2410.02630/) (2024)
- [Zero-shot capability of SAM-family models for bone segmentation in CT scans](https://mlsanity.com/paper/2411.08629/) (2024)
- [An Efficient Dual-Line Decoder Network with Multi-Scale Convolutional Attention for Multi-organ Segmentation](https://mlsanity.com/paper/2508.17007/) (2025)
- [Deep learning-based brain segmentation model performance validation with clinical radiotherapy CT](https://mlsanity.com/paper/2406.17423/) (2024)

## Abstract (arXiv)

In response to a concerning trend of selectively emphasizing metrics in medical image segmentation (MIS) studies, we introduce textttseg-metrics, an open-source Python package for standardized MIS model evaluation. Unlike existing packages, textttseg-metrics offers user-friendly interfaces for various overlap-based and distance-based metrics, providing a comprehensive solution. textttseg-metrics supports multiple file formats and is easily installable through the Python Package Index (PyPI). With a focus on speed and convenience, textttseg-metrics stands as a valuable tool for efficient MIS model assessment.