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AnyMetric: Universal Metric Optimization for Medical Image Segmentation With Preference Learning.

Chaoqun Wang, Jiawei Mo, Shaobo Min

IEEE Journal of Biomedical and Health Informatics

|June 10, 2026

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Preference-Guided Segmentation Network (PGSNet) optimizes medical image segmentation by learning human preferences, avoiding complex surrogate loss functions. This approach achieves superior segmentation results across various metrics and datasets.

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Accurate medical image segmentation is vital for disease diagnosis and treatment planning.
Current methods rely on complex, handcrafted surrogate loss functions for optimizing segmentation metrics.
Customizing loss functions for diverse and non-differentiable metrics is challenging and labor-intensive.

Purpose of the Study:

To introduce a novel framework, Preference-Guided Segmentation Network (PGSNet), for unified optimization of arbitrary medical image segmentation metrics.
To eliminate the need for handcrafted surrogate loss functions by directly learning evaluation preferences.
To provide a generalizable and flexible optimization framework for various segmentation metrics.

Main Methods:

PGSNet generates diverse segmentation candidates using multi-scale representations.

Related Experiment Videos

Last Updated: Jun 12, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

A reward function, derived from target evaluation metrics, ranks these candidates based on segmentation preferences.

Preference signals guide model training to favor superior segmentation outcomes over inferior ones.

Main Results:

PGSNet achieves superior segmentation results compared to existing methods on challenging public datasets.
The framework demonstrates effective optimization across various arbitrary evaluation metrics.
Experiments confirm the generalizability and flexibility of the proposed preference-based optimization strategy.

Conclusions:

PGSNet offers a novel and effective approach to medical image segmentation optimization.
The framework successfully bypasses the limitations of handcrafted surrogate loss functions.
PGSNet provides a flexible and generalizable solution for optimizing diverse segmentation evaluation metrics.