Accurate detection and localization of traumatic injuries in abdominal CT remain challenging because voxel-level annotations are limited and expensive to obtain. We present a label-efficient framework for 3D abdominal trauma detection that combines self-supervised pretraining with semi-supervised transformer-based detection. First, we use Masked Image Modeling (MIM) on 1098 CT volumes to pretrain a 3D U-Net encoder for anatomical representation learning. Next, we adapt V-DETR to dense volumetric CT through a feature adapter that converts the encoder feature grid into a compact token sequence for transformer decoding. The pretrained encoder is then integrated with V-DETR and 3D Vertex Relative Position Encoding (3D V-RPE) to improve the localization of irregularly shaped injuries. Finally, semi-supervised teacher-student consistency regularization leverages 2,000 additional unlabeled volumes during detector training. To the best of our knowledge, this is the first application of a 3D DETR-style detector to the RSNA abdominal trauma detection task. On this benchmark, the proposed method achieves 31.33% test [email protected] using only 78 labeled training volumes, corresponding to a 1.53x improvement over supervised-only training. These results show that combining medical-domain pretraining with semi-supervised learning is an effective strategy for label-scarce 3D medical detection.

翻译：[translated abstract in Chinese] 腹部CT中创伤性损伤的精确检测与定位仍具有挑战性，因为体素级标注既稀缺又昂贵。我们提出了一种标签高效的3D腹部创伤检测框架，该框架将自监督预训练与基于半监督Transformer的检测相结合。首先，我们在1098个CT体数据上采用掩码图像建模（MIM）预训练3D U-Net编码器，以学习解剖学表示。其次，通过特征适配器将V-DETR适配至稠密体积CT，将编码器特征网格转换为紧凑的标记序列用于Transformer解码。随后，将预训练编码器与V-DETR及3D顶点相对位置编码（3D V-RPE）集成，以改善不规则形状损伤的定位。最后，半监督师生一致性正则化在检测器训练中利用了2000个额外的未标记体数据。据我们所知，这是首个将3D DETR类检测器应用于RSNA腹部创伤检测任务的研究。在该基准测试中，所提方法仅使用78个带标签训练体数据即达到31.33%的测试[email protected]，相比纯监督训练提升了1.53倍。这些结果表明，将医学领域预训练与半监督学习相结合是应对3D医学检测中标签稀缺问题的有效策略。