Recent advances in foundation models have revolutionized model development in digital pathology, reducing dependence on extensive manual annotations required by traditional methods. The ability of foundation models to generalize well with few-shot learning addresses critical barriers in adapting models to diverse medical imaging tasks. This work presents the Granular Box Prompt Segment Anything Model (GB-SAM), an improved version of the Segment Anything Model (SAM) fine-tuned using granular box prompts with limited training data. The GB-SAM aims to reduce the dependency on expert pathologist annotators by enhancing the efficiency of the automated annotation process. Granular box prompts are small box regions derived from ground truth masks, conceived to replace the conventional approach of using a single large box covering the entire H&E-stained image patch. This method allows a localized and detailed analysis of gland morphology, enhancing the segmentation accuracy of individual glands and reducing the ambiguity that larger boxes might introduce in morphologically complex regions. We compared the performance of our GB-SAM model against U-Net trained on different sizes of the CRAG dataset. We evaluated the models across histopathological datasets, including CRAG, GlaS, and Camelyon16. GB-SAM consistently outperformed U-Net, with reduced training data, showing less segmentation performance degradation. Specifically, on the CRAG dataset, GB-SAM achieved a Dice coefficient of 0.885 compared to U-Net’s 0.857 when trained on 25% of the data. Additionally, GB-SAM demonstrated segmentation stability on the CRAG testing dataset and superior generalization across unseen datasets, including challenging lymph node segmentation in Camelyon16, which achieved a Dice coefficient of 0.740 versus U-Net’s 0.491. Furthermore, compared to SAM-Path and Med-SAM, GB-SAM showed competitive performance. GB-SAM achieved a Dice score of 0.900 on the CRAG dataset, while SAM-Path achieved 0.884. On the GlaS dataset, Med-SAM reported a Dice score of 0.956, whereas GB-SAM achieved 0.885 with significantly less training data. These results highlight GB-SAM’s advanced segmentation capabilities and reduced dependency on large datasets, indicating its potential for practical deployment in digital pathology, particularly in settings with limited annotated datasets.
基础模型的最新进展彻底改变了数字病理学中的模型开发，减少了对传统方法所需大量人工标注的依赖。基础模型通过小样本学习展现出的良好泛化能力，解决了模型适应多样化医学影像任务的关键障碍。本研究提出粒度框提示分割任意模型（GB-SAM），该模型是基于分割任意模型（SAM）的改进版本，通过使用有限训练数据下的粒度框提示进行微调。GB-SAM旨在通过提升自动标注过程的效率，降低对专业病理学家标注者的依赖。粒度框提示是从真实标注掩膜中提取的小型框区域，旨在替代传统使用覆盖整个H&E染色图像切片的单一大型框的方法。这种方法允许对腺体形态进行局部精细分析，提升单个腺体的分割精度，并减少大尺寸框在形态复杂区域可能引入的歧义性。我们将GB-SAM模型与在不同规模CRAG数据集上训练的U-Net进行性能比较，并在包括CRAG、GlaS和Camelyon16在内的组织病理学数据集上进行评估。GB-SAM在使用较少训练数据的情况下持续优于U-Net，且分割性能下降更小。具体而言，在CRAG数据集上，当使用25%数据训练时，GB-SAM的Dice系数达到0.885，而U-Net为0.857。此外，GB-SAM在CRAG测试数据集上表现出分割稳定性，并在未见数据集上具有卓越的泛化能力，包括在Camelyon16中具有挑战性的淋巴结分割任务（Dice系数达0.740，U-Net为0.491）。与SAM-Path和Med-SAM相比，GB-SAM展现出具有竞争力的性能：在CRAG数据集上GB-SAM的Dice分数为0.900，而SAM-Path为0.884；在GlaS数据集上，Med-SAM报告的Dice分数为0.956，而GB-SAM在使用显著更少训练数据的情况下达到0.885。这些结果凸显了GB-SAM先进的分割能力和对大规模数据集依赖性的降低，表明其在数字病理学中实际部署的潜力，特别是在标注数据集有限的环境中。
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