Xiaoyan Luo

Miao Zhu, Qiming Fu, Bo Liu et al.

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiration limits its accuracy. Recently, 3D imaging from a single X-ray projection has received extensive attention as a promising approach to address this issue. However, current methods can only reconstruct 3D images without directly locating the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirements of motion control in radiotherapy. In this study, a novel imaging method RT-SRTS is proposed which integrates 3D imaging and tumor segmentation into one network based on multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from a single X-ray projection at any angle. Furthermore, the attention enhanced calibrator (AEC) and uncertain-region elaboration (URE) modules have been proposed to aid feature extraction and improve segmentation accuracy. The proposed method was evaluated on fifteen patient cases and compared with three state-of-the-art methods. It not only delivers superior 3D reconstruction but also demonstrates commendable tumor segmentation results. Simultaneous reconstruction and segmentation can be completed in approximately 70 ms, significantly faster than the required time threshold for real-time tumor tracking. The efficacies of both AEC and URE have also been validated in ablation studies. The code of work is available at https://github.com/ZywooSimple/RT-SRTS.

Jincheng Yan, Yun Wang, Xiaoyan Luo et al.

Person re-identification (ReID) plays a critical role in applications such as security surveillance and criminal investigations. Most traditional image-based ReID methods face challenges including occlusions and lighting changes, while text provides complementary information to mitigate these issues. However, the integration of both image and text modalities remains underexplored. To address this gap, we propose {\bf PS-ReID}, a multimodal model that combines image and text inputs to enhance ReID performance. In contrast to existing ReID methods limited by cropped pedestrian images, our PS-ReID focuses on full-scene settings and introduces a multimodal ReID task that incorporates segmentation, enabling precise feature extraction of the queried individual, even under challenging conditions such as occlusion. To this end, our model adopts a dual-path asymmetric encoding scheme that explicitly separates query and target roles: the query branch captures identity-discriminative cues, while the target branch performs holistic scene reasoning. Additionally, a token-level ReID loss supervises identity-aware tokens, coupling retrieval and segmentation to yield masks that are both spatially precise and identity-consistent. To facilitate systematic evaluation, we construct M2ReID, currently the largest full-scene multimodal ReID dataset, with over 200K images and 4,894 identities, featuring multimodal queries and high-quality segmentation masks. Experimental results demonstrate that PS-ReID significantly outperforms unimodal query-based models in both ReID and segmentation tasks. The model excels in challenging real-world scenarios such as occlusion, low lighting, and background clutter, offering a robust and flexible solution for person retrieval and segmentation. All code, models, and datasets will be publicly available.