Towards Voxel Spacing Consistency for Medical Image Segmentation

2026-06-30

AI Summary

This work addresses the adverse impact of inconsistent voxel spacing on medical image segmentation performance, a challenge exacerbated by existing resampling methods that neglect coordination with downstream tasks. To overcome this limitation, the authors propose Consispace, a novel framework that uniquely integrates anatomical continuity and class-level semantic consistency into the resampling process. Specifically, Consispace employs an ODE-based continuous interpolator to model inter-slice dynamics, leverages dense features from pretrained vision models to guide semantically coherent resampling, and incorporates an implicit neural representation to enable interpolation at arbitrary scales. Extensive experiments demonstrate that the proposed method significantly enhances resampling quality, improves inter-slice anatomical smoothness, and boosts downstream segmentation performance across multiple datasets.

Abstract

Volumetric medical image segmentation is essential for both preoperative diagnosis and intraoperative guidance. While recent years have witnessed rapid progress in segmentation architectures, comparatively little attention is paid to the physical voxel spacing of anatomical data. Indeed, volumetric image resampling is a ubiquitous preprocessing step before segmentation, yet its interaction with downstream segmentation has not been systematically exploited. In this work, we study the correlation between image resampling and segmentation, and propose Consispace, a semantic-aware resampling framework that achieves consistent voxel spacing in the axial direction while preserving anatomical and semantic consistency. Consispace introduces an ODE-based anatomical constraint to model inter-slice dynamics with a continuous interpolator, enabling faithful reconstruction under complex anatomical transitions beyond discrete interpolation. To further couple resampling with segmentation objectives, we leverage dense features from a pretrained vision model to build intra-slice semantic correlation maps and inject class-wise semantic consistency via feature reweighting during resampling. Both intra-slice and inter-slice constraints are integrated into an implicit neural network, supporting arbitrary-scale resampling. Extensive experiments on multiple datasets demonstrate that Consispace achieves superior reconstruction quality and perceptual fidelity, produces smoother inter-slice anatomy, and improves downstream segmentation performance when used as a preprocessing step.

Problem

• voxel spacing
• medical image segmentation
• image resampling
• anatomical consistency
• semantic consistency

Innovation

• semantic-aware resampling
• voxel spacing consistency
• ODE-based interpolation
• implicit neural network
• medical image segmentation