标题： 使用通用分割模型进行医学图像的标志性检测 显示英文标题

标题： Landmark Detection for Medical Images using a General-purpose Segmentation Model

摘要： 放射图像在骨科医学诊断中是核心工具，解剖标志点检测作为信息提取的关键中间步骤。 通用基础分割模型，如SAM（Segment Anything Model），默认不支持标志点分割，需要提示才能运行。 然而，在医学影像中，标志点的提示非常具体。 由于SAM未接受过此类标志点的训练，因此无法为诊断目的生成准确的标志点分割。 即使MedSAM，SAM的医学适应变体，也仅被训练用于识别较大的解剖结构，如器官及其部分，缺乏骨科骨盆标志点所需的细粒度精度。 为解决这一限制，我们提出利用另一个通用的非基础模型：YOLO。 YOLO在目标检测方面表现出色，可以提供作为SAM输入提示的边界框。 虽然YOLO在检测方面效率高，但在分割复杂结构方面明显不如SAM。 结合使用，这两个模型形成了一条可靠的流水线，能够分割不仅包括八个解剖标志点的小型试点集，还包括72个标志点和16个具有复杂轮廓的区域，如股骨皮质骨和骨盆入口。 通过使用YOLO生成的边界框引导SAM，我们训练了混合模型以准确分割骨科骨盆X射线图像。 我们的结果表明，所提出的YOLO和SAM组合在检测骨科骨盆X射线图像中的解剖标志点和复杂轮廓方面表现出色。 显示英文摘要

摘要： Radiographic images are a cornerstone of medical diagnostics in orthopaedics, with anatomical landmark detection serving as a crucial intermediate step for information extraction. General-purpose foundational segmentation models, such as SAM (Segment Anything Model), do not support landmark segmentation out of the box and require prompts to function. However, in medical imaging, the prompts for landmarks are highly specific. Since SAM has not been trained to recognize such landmarks, it cannot generate accurate landmark segmentations for diagnostic purposes. Even MedSAM, a medically adapted variant of SAM, has been trained to identify larger anatomical structures, such as organs and their parts, and lacks the fine-grained precision required for orthopaedic pelvic landmarks. To address this limitation, we propose leveraging another general-purpose, non-foundational model: YOLO. YOLO excels in object detection and can provide bounding boxes that serve as input prompts for SAM. While YOLO is efficient at detection, it is significantly outperformed by SAM in segmenting complex structures. In combination, these two models form a reliable pipeline capable of segmenting not only a small pilot set of eight anatomical landmarks but also an expanded set of 72 landmarks and 16 regions with complex outlines, such as the femoral cortical bone and the pelvic inlet. By using YOLO-generated bounding boxes to guide SAM, we trained the hybrid model to accurately segment orthopaedic pelvic radiographs. Our results show that the proposed combination of YOLO and SAM yields excellent performance in detecting anatomical landmarks and intricate outlines in orthopaedic pelvic radiographs.