Medical image processing usually requires a model trained with carefully
crafted datasets due to unique image characteristics and domain-specific
challenges, especially in pathology. Primitive detection and segmentation in
digitized tissue samples are essential for objective and automated diagnosis
and prognosis of cancer. SAM (Segment Anything Model) has recently been
developed to segment general objects from natural images with high accuracy,
but it requires human prompts to generate masks. In this work, we present a
novel approach that adapts pre-trained natural image encoders of SAM for
detection-based region proposals. Regions proposed by a pre-trained encoder are
sent to cascaded feature propagation layers for projection. Then, local
semantic and global context is aggregated from multi-scale for bounding box
localization and classification. Finally, the SAM decoder uses the identified
bounding boxes as essential prompts to generate a comprehensive primitive
segmentation map. The entire base framework, SAM, requires no additional
training or fine-tuning but could produce an end-to-end result for two
fundamental segmentation tasks in pathology. Our method compares with
state-of-the-art models in F1 score for nuclei detection and binary/multiclass
panoptic(bPQ/mPQ) and mask quality(dice) for segmentation quality on the
PanNuke dataset while offering end-to-end efficiency. Our model also achieves
remarkable Average Precision (+4.5%) on the secondary dataset (HuBMAP Kidney)
compared to Faster RCNN. The code is publicly available at
this https URL

通过对 SAM 进行自适应预训练图像编码器以进行基于检测的区域提议，我们的方法在病理学领域的两个基本分割任务中达到了与最先进模型相媲美的 F1 得分（细胞核检测）和二进制 / 多类别 panoptic（bPQ/mPQ）和掩模质量（dice），同时实现了端到端效率。