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arXiv · 2510.09499

A methodology for clinically driven interactive segmentation evaluation

Parhom Esmaeili·Virginia Fernandez·Pedro Borges·Eli Gibson·Sebastien Ourselin·M. Jorge Cardoso
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Abstract

Interactive segmentation is a promising strategy for building robust, generalisable algorithms for volumetric medical image segmentation. However, inconsistent and clinically unrealistic evaluation hinders fair comparison and misrepresents real-world performance. We propose a clinically grounded methodology for defining evaluation tasks and metrics, and built a software framework for constructing standardised evaluation pipelines. We evaluate state-of-the-art algorithms across heterogeneous and complex tasks and observe that (i) minimising information loss when processing user interactions is critical for model robustness, (ii) adaptive-zooming mechanisms boost robustness and speed convergence, (iii) performance drops if validation prompting behaviour/budgets differ from training, (iv) 2D methods perform well with slab-like images and coarse targets, but 3D context helps with large or irregularly shaped targets, (v) performance of non-medical-domain models (e.g. SAM2) degrades with poor contrast and complex shapes.

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Parhom Esmaeili, Virginia Fernandez, Pedro Borges, Eli Gibson, Sebastien Ourselin, M. Jorge Cardoso. 2025-10-10. A methodology for clinically driven interactive segmentation evaluation. https://arxiv.org/abs/2510.09499

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