RadYOLO：面向CT和MRI的计算高效型3D目标检测与分割方法

中文摘要 AI 辅助

三维医学图像的目标检测与分割是当前研究的热点领域。然而，多数已提出的深度学习模型计算成本高昂，且很少有模型能同时具备广泛适用性、高检测性能，以及在资源受限硬件上快速运行的能力。为解决这一缺口，本文提出RadYOLO，它是针对医学图像定制的YOLO11的3D扩展版本。我们在包含CT和MRI数据的5个数据集上，将其与nnU-Net和nnDetection进行对比，这些数据集涵盖不同目标尺寸和患病率。RadYOLO在5个数据集中的4个上，检测性能优于nnDetection，在剩余1个上表现相当；与nnU-Net相比，RadYOLO在病灶检测任务上表现更好，而当需要精确定位大器官时，nnU-Net更具优势；当仅需粗略目标定位时，RadYOLO在全部5个数据集上的表现与nnU-Net相当或更优。在GPU上，RadYOLO的推理速度比nnU-Net快8至46倍，相比nnDetection的速度提升更为显著；在CPU上运行时，RadYOLO的推理仅需数秒，仍比GPU上的nnU-Net更快，为临床应用和边缘设备部署提供了显著优势。RadYOLO的代码仓库可通过该URL访问。

英文摘要

Object detection and segmentation in three-dimensional medical images is a very active area of research. However, most proposed deep learning models carry a high computational cost, and only few aim to be broadly applicable, achieve high detection performance, and remain fast to execute on resource-constrained hardware. To address this gap, we present RadYOLO, a 3D extension of YOLO11 tailored to medical images. We compare it with nnU-Net and nnDetection on five datasets comprising CT and MRI data with varying object sizes and prevalence. RadYOLO's detection performance surpasses that of nnDetection on four of five datasets and is comparable on one. Compared to nnU-Net, RadYOLO performs better on lesion detection tasks, while nnU-Net excels at detecting large organs when precise localization is required. When rough object localization is sufficient, RadYOLO matches or outperforms nnU-Net on all five datasets. Regarding inference time, RadYOLO is 8-46x faster than nnU-Net on a GPU. Compared to nnDetection the speedup is even higher. When executed on a CPU, RadYOLO's inference runs within seconds (still faster than nnU-Net on a GPU) offering a significant advantage for clinical and edge-device deployment. RadYOLO repository: https://github.com/FraunhoferMEVIS/RadYOLO

发表机构

• Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）

机构由 AI 辅助整理，请以论文原文为准。