动态剪枝提升3D医学图像分割效率，兼顾精度与资源消耗。
Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation
- 从冗余模型出发，分步剪枝并用功能解耦损失优化结构
- 轻量版PSP-Seg-S在5个数据集上参数减少83%-87%，训练提速29%-48%
- 适合临床快速部署的高效分割任务，尤其关注算力受限场景
3D医学图像分割常因资源和时间开销大，难以在临床环境中规模化应用。现有高效模型多为静态、人工设计，缺乏跨任务适应性，难平衡性能与效率。本文提出PSP-Seg，一种渐进式剪枝框架，实现动态高效的3D分割。PSP-Seg从冗余模型开始，结合块级剪枝与功能解耦损失，迭代剪除冗余模块。在五个公开数据集上评估，对比七种先进模型及六种高效模型，结果表明轻量版PSP-Seg-S在所有数据集上性能接近nnU-Net，同时降低GPU内存使用42%-45%、训练时间29%-48%、参数量83%-87%。该成果彰显其作为低成本高表现模型在临床广泛应用的潜力。
原文摘要 · Abstract (English)
3D medical image segmentation often faces heavy resource and time consumption, limiting its scalability and rapid deployment in clinical environments. Existing efficient segmentation models are typically static and manually designed prior to training, which restricts their adaptability across diverse tasks and makes it difficult to balance performance with resource efficiency. In this paper, we propose PSP-Seg, a progressive pruning framework that enables dynamic and efficient 3D segmentation. PSP-Seg begins with a redundant model and iteratively prunes redundant modules through a combination of block-wise pruning and a functional decoupling loss. We evaluate PSP-Seg on five public datasets, benchmarking it against seven state-of-the-art models and six efficient segmentation models. Results demonstrate that the lightweight variant, PSP-Seg-S, achieves performance on par with nnU-Net while reducing GPU memory usage by 42-45%, training time by 29-48%, and parameter number by 83-87% across all datasets. These findings underscore PSP-Seg's potential as a cost-effective yet high-performing alternative for widespread clinical application.
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