Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation
This provides a cost-effective solution for scalable clinical deployment of 3D medical image segmentation, though it is incremental as it builds on existing pruning techniques.
The paper tackles the problem of heavy resource and time consumption in 3D medical image segmentation by proposing PSP-Seg, a progressive pruning framework that achieves performance comparable to nnU-Net while reducing GPU memory usage by 42-45%, training time by 29-48%, and parameters by 83-87% across five datasets.
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.