AutoImageSeg: A zero-code image segmentation software toolkit

SoftwareX 2026 Open access

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Authors: Weihao Gao, Jiarou Lu
DOI: 10.1016/j.softx.2025.102491
Cited by: 1
References: 16

Abstract

AutoImageSeg is a zero-code, open-source image segmentation software toolkit that integrates nine mainstream models. It offers a closed-loop workflow encompassing training, inference, evaluation, and re-annotation. Through its graphical user interface (GUI), users can effortlessly benchmark models, predict new data, and auto-generate editable LabelMe labels—all without any programming. This streamlined process facilitates rapid iteration and high-quality ground-truth accumulation, especially in small-sample scenarios. By accelerating dataset construction across multiple domains, AutoImageSeg serves as a powerful tool for both researchers and industry professionals.

Fields of Study & Subjects

Advanced Neural Network Applications Machine Learning and Data Classification Cell Image Analysis Techniques

Publication details

Journal / source
SoftwareX
Publisher
Elsevier BV
Volume
33
Pages
102491–102491
Publication date
6 January 2026
Language
EN