用SAM模型提升皮肤癌切片图像自动分割精度
Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images
- 先用Segformer生成初步掩码，再用动态提示策略引导SAM
- 在高分辨率切片上实现9.1%的IoU提升，优于现有方法
- 适合医学图像分析与自动化病理诊断研究者使用
皮肤癌在全切片图像（WSI）中的分割有助于预后判断及关键预后指标如Breslow深度和原发浸润肿瘤尺寸的测量。本文提出一种新方法，利用分割一切模型（SAM）实现显微切片图像中皮肤癌的自动分割。该方法首先采用语义分割模型生成初始分割掩码，用于提示SAM；设计动态提示策略，结合质心与网格提示，在保持提示质量的同时实现对超高清切片图像的最佳覆盖。为优化侵袭性皮肤癌分割，进一步引入原位皮肤癌检测与低置信度区域过滤机制。选用Segformer作为初始分割模型，EfficientSAM作为分割一切模型以实现参数高效的微调。实验结果表明，该方法不仅超越其他先进皮肤癌分割方法，且相较基线模型Segformer在IoU上提升9.1%。
原文摘要 · Abstract (English)
Melanoma segmentation in Whole Slide Images (WSIs) is useful for prognosis and the measurement of crucial prognostic factors such as Breslow depth and primary invasive tumor size. In this paper, we present a novel approach that uses the Segment Anything Model (SAM) for automatic melanoma segmentation in microscopy slide images. Our method employs an initial semantic segmentation model to generate preliminary segmentation masks that are then used to prompt SAM. We design a dynamic prompting strategy that uses a combination of centroid and grid prompts to achieve optimal coverage of the super high-resolution slide images while maintaining the quality of generated prompts. To optimize for invasive melanoma segmentation, we further refine the prompt generation process by implementing in-situ melanoma detection and low-confidence region filtering. We select Segformer as the initial segmentation model and EfficientSAM as the segment anything model for parameter-efficient fine-tuning. Our experimental results demonstrate that this approach not only surpasses other state-of-the-art melanoma segmentation methods but also significantly outperforms the baseline Segformer by 9.1% in terms of IoU.
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