RadYOLO实现高效3D医学图像检测，速度比nnU-Net快46倍
RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI
- 基于YOLO11设计3D医疗影像检测模型，兼顾精度与速度
- 在5个数据集上检测性能优于nnDetection，且推理速度快8-46倍
- 适合临床部署和边缘设备，CPU上推理仅需数秒
三维医学图像中的目标检测与分割是活跃研究领域。然而，多数深度学习模型计算开销高，仅有少数模型兼具广泛适用性、高检测性能与资源受限硬件上的快速执行能力。为填补这一空白，我们提出RadYOLO，即针对医学图像优化的YOLO11 3D扩展。我们在包含CT和MRI数据的五个数据集上将其与nnU-Net和nnDetection进行比较，这些数据集涵盖不同大小与出现频率的目标。RadYOLO在四个数据集上检测性能超过nnDetection，一个数据集上相当；相比nnU-Net，在病灶检测任务中表现更优，而nnU-Net在需要精确定位的大器官检测中占优。当粗略定位即可时，RadYOLO在全部五个数据集上达到或超越nnU-Net性能。推理时间方面，其在GPU上比nnU-Net快8-46倍，对比nnDetection速度提升更高；在CPU上推理可在数秒内完成，仍快于nnU-Net在GPU上的表现，显著利于临床与边缘设备部署。
原文摘要 · Abstract (English)
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
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