mlsanityData to 7 Oct 2026

Seg-metrics: a Python package to compute segmentation metrics

arXiv 2403.07884 ↗First posted 12 Jan 2024cs.CV cs.AIcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 1 of its 12 references that are in the map. First posted on arXiv 12 Jan 2024.

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1 of 12 references.

References · 1

  1. 2022Towards a Guideline for Evaluation Metrics in Medical Image Segmentationcited by 58

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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024Understanding implementation pitfalls of distance-based metrics for image segmentationcited by 3
  2. 2024Zero-shot capability of SAM-family models for bone segmentation in CT scanscited by 3
  3. 2025An Efficient Dual-Line Decoder Network with Multi-Scale Convolutional Attention for Multi-organ Segmentationcited by 1
  4. 2024Deep learning-based brain segmentation model performance validation with clinical radiotherapy CTcited by 0