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Abstract：
Colorectal cancer (CRC) is the third most common cancer in the United States. Tumor Budding (TB) detection and quantification are crucial yet labor-intensive steps in determining the CRC stage through the analysis of histopathology images. To help with this process, we adapt the Segment Anything Model (SAM) on the CRC histopathology images to segment TBs using SAM-Adapter. In this approach, we automatically take task-specific prompts from CRC images and train the SAM model in a parameter-efficient way. We compare the predictions of our model with the predictions from a trained-from-scratch model using the annotations from a pathologist. As a result, our model achieves an intersection over union (IoU) of 0.65 and an instance-level Dice score of 0.75, which are promising in matching the pathologist\'s TB annotation. We believe our study offers a novel solution to identify TBs on H&E-stained histopathology images. Our study also demonstrates the value of adapting the foundation model for pathology image segmentation tasks.
摘要：
结直肠癌（CRC）是美国第三大最常见的癌症。肿瘤出芽（TB）检测和定量是通过组织病理学图像分析确定CRC阶段的关键但劳动密集型步骤。为了帮助这个过程，我们使用SAM-Adapter对CRC组织病理学图像上的任意段模型（SAM）进行调整以分割TB。在这种方法中，我们会自动从CRC图像中获取特定于任务的提示，并以参数有效的方式训练SAM模型。我们使用病理学家的注释将模型的预测与从零开始训练的模型的预测进行比较。因此,我们的模型实现了0.65的交集联合(IoU)和0.75的实例级Dice评分,这在匹配病理学家的TB注释方面是有希望的。我们相信我们的研究提供了一种新的解决方案来识别H＆E染色的组织病理学图像上的TBs。我们的研究还证明了将基础模型用于病理图像分割任务的价值。
