PDF(Pubmed)
Abstract：
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.
摘要：
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，减少对传统方法所需的大量手动注释的依赖。基础模型通过少量学习来很好地概括的能力解决了使模型适应各种医学成像任务的关键障碍。这项工作提出了颗粒盒提示段任意模型(GB-SAM)，段任意模型(SAM)的改进版本，使用粒度框提示和有限的训练数据进行微调。GB-SAM旨在通过提高自动注释过程的效率来减少对专家病理学家注释器的依赖。颗粒框提示是源自地面实况蒙版的小框区域，设想取代使用覆盖整个H＆E染色图像块的单个大框的常规方法。这种方法允许腺体形态的局部和详细分析，提高了单个腺体的分割精度，并减少了较大的盒子可能在形态复杂的区域中引入的歧义。我们比较了GB-SAM模型与在不同大小的CRAG数据集上训练的U-Net的性能。我们评估了组织病理学数据集的模型，包括CRAG,Glas,和Camelyon16.GB-SAM的表现始终优于U-Net，随着训练数据的减少，显示较少的分段性能下降。具体来说,在CRAG数据集上，当在25%的数据上训练时,GB-SAM获得的骰子系数为0.885,而U-Net为0.857。此外,GB-SAM在CRAG测试数据集上展示了分段稳定性，并且在看不见的数据集上具有出色的泛化能力。包括在Camelyon16中具有挑战性的淋巴结分割，其Dice系数为0.740，而U-Net为0.491。此外，与SAM-Path和Med-SAM相比，GB-SAM表现出竞争力。GB-SAM在CRAG数据集上的骰子得分为0.900，而SAM-Path达到0.884。在GLS数据集上，Med-SAM报告Dice得分为0.956，而GB-SAM获得0.885，训练数据明显较少。这些结果突出了GB-SAM的高级分段功能和对大型数据集的减少的依赖，表明其在数字病理学中的实际应用潜力，特别是在具有有限注释数据集的设置中。
