Autoregressive Medical Image Segmentation via Next-Scale Mask Prediction

• URL: http://arxiv.org/abs/2502.20784v1
• Date: Fri, 28 Feb 2025 07:05:58 GMT
• Title: Autoregressive Medical Image Segmentation via Next-Scale Mask Prediction
• Authors: Tao Chen, Chenhui Wang, Zhihao Chen, Hongming Shan,
• Abstract summary: We propose the AutoRegressive framework via next-scale mask prediction, termed AR-Seg.<n>AR-Seg progressively predicts the next-scale mask by explicitly modeling dependencies across all previous scales.<n>We show that AR-Seg outperforms state-of-the-art methods while explicitly visualizing the intermediate coarse-to-fine segmentation process.
• Score: 16.026171689438637
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While deep learning has significantly advanced medical image segmentation, most existing methods still struggle with handling complex anatomical regions. Cascaded or deep supervision-based approaches attempt to address this challenge through multi-scale feature learning but fail to establish sufficient inter-scale dependencies, as each scale relies solely on the features of the immediate predecessor. To this end, we propose the AutoRegressive Segmentation framework via next-scale mask prediction, termed AR-Seg, which progressively predicts the next-scale mask by explicitly modeling dependencies across all previous scales within a unified architecture. AR-Seg introduces three innovations: (1) a multi-scale mask autoencoder that quantizes the mask into multi-scale token maps to capture hierarchical anatomical structures, (2) a next-scale autoregressive mechanism that progressively predicts next-scale masks to enable sufficient inter-scale dependencies, and (3) a consensus-aggregation strategy that combines multiple sampled results to generate a more accurate mask, further improving segmentation robustness. Extensive experimental results on two benchmark datasets with different modalities demonstrate that AR-Seg outperforms state-of-the-art methods while explicitly visualizing the intermediate coarse-to-fine segmentation process.

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