Lightweight Method for Interactive 3D Medical Image Segmentation with Multi-Round Result Fusion

• URL: http://arxiv.org/abs/2412.08315v1
• Date: Wed, 11 Dec 2024 11:52:16 GMT
• Title: Lightweight Method for Interactive 3D Medical Image Segmentation with Multi-Round Result Fusion
• Authors: Bingzhi Shen, Lufan Chang, Siqi Chen, Shuxiang Guo, Hao Liu,
• Abstract summary: Segment Anything Model (SAM) has drawn widespread attention due to its zero-shot generalization capabilities in interactive segmentation. We propose Lightweight Interactive Network for 3D Medical Image (LIM-Net) as a novel approach demonstrating the potential of compact CNN-based models. LIM-Net initiates segmentation by generating a 2D prompt mask from user hints. It exhibits stronger generalization to unseen data compared to SAM-based models, with competitive accuracy while requiring fewer interactions.
• Score: 7.158573385931718
• License:
• Abstract: In medical imaging, precise annotation of lesions or organs is often required. However, 3D volumetric images typically consist of hundreds or thousands of slices, making the annotation process extremely time-consuming and laborious. Recently, the Segment Anything Model (SAM) has drawn widespread attention due to its remarkable zero-shot generalization capabilities in interactive segmentation. While researchers have explored adapting SAM for medical applications, such as using SAM adapters or constructing 3D SAM models, a key question remains: Can traditional CNN networks achieve the same strong zero-shot generalization in this task? In this paper, we propose the Lightweight Interactive Network for 3D Medical Image Segmentation (LIM-Net), a novel approach demonstrating the potential of compact CNN-based models. Built upon a 2D CNN backbone, LIM-Net initiates segmentation by generating a 2D prompt mask from user hints. This mask is then propagated through the 3D sequence via the Memory Module. To refine and stabilize results during interaction, the Multi-Round Result Fusion (MRF) Module selects and merges optimal masks from multiple rounds. Our extensive experiments across multiple datasets and modalities demonstrate LIM-Net's competitive performance. It exhibits stronger generalization to unseen data compared to SAM-based models, with competitive accuracy while requiring fewer interactions. Notably, LIM-Net's lightweight design offers significant advantages in deployment and inference efficiency, with low GPU memory consumption suitable for resource-constrained environments. These promising results demonstrate LIM-Net can serve as a strong baseline, complementing and contrasting with popular SAM models to further boost effective interactive medical image segmentation. The code will be released at \url{https://github.com/goodtime-123/LIM-Net}.

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