改进医学图像分割模型在小目标和小框提示下的准确性
Optimization of MedSAM model based on bounding box adaptive perturbation algorithm
- 引入自适应边界框扰动算法优化训练过程
- 显著降低小目标与邻近结构的误分割率
- 适合需要高精度分割的医学影像分析场景
MedSAM模型基于SAM框架，通过通用化训练提升医学图像分割能力，但仍存在明显局限。首先，训练中扰动窗口设置受限，导致小组织或器官易与邻近结构错误合并分割，引发分割误差。其次，面对形状不规则、结构复杂的医学图像目标时，通常需缩小边界框以明确分割意图，但此时MedSAM性能仍不理想。为此，本研究提出一种边界框自适应扰动算法，优化训练流程，旨在减少小目标的分割错误，并提升模型在缩小边界框提示下的准确率，从而增强MedSAM在复杂医学影像任务中的鲁棒性与可靠性。
原文摘要 · Abstract (English)
The MedSAM model, built upon the SAM framework, enhances medical image segmentation through generalizable training but still exhibits notable limitations. First, constraints in the perturbation window settings during training can cause MedSAM to incorrectly segment small tissues or organs together with adjacent structures, leading to segmentation errors. Second, when dealing with medical image targets characterized by irregular shapes and complex structures, segmentation often relies on narrowing the bounding box to refine segmentation intent. However, MedSAM's performance under reduced bounding box prompts remains suboptimal. To address these challenges, this study proposes a bounding box adaptive perturbation algorithm to optimize the training process. The proposed approach aims to reduce segmentation errors for small targets and enhance the model's accuracy when processing reduced bounding box prompts, ultimately improving the robustness and reliability of the MedSAM model for complex medical imaging tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。