Yuxuan Yao

Physics in Medicine & Biology

Dynamic iterative coarse-to-fine prompt learning based on SAM for precise esophageal cancer gross target volume segmentation

Accurate segmentation of the gross tumor volume from computed tomography images is a core step in the development of precise radiotherapy planning for esophageal cancer, which directly affects treatment efficacy and normal tissue protection. Recently, foundation models represented by the segment anything model (SAM) ha...

Analyte-Specific "Signal-On" Uranyl Sensitive Sensing Based on Temperature-Programmed Nanozyme Activity of CoOx.

As alternatives to bioenzymes, nanozymes find promising applications in biochemical sensing thanks to their recognized superiorities, but their lack of specific interactions with targets of interest seriously hinders the exploration of selective analytical strategies when no external receptors are employed. Additionall...

Dynamic iterative coarse-to-fine prompt learning based on SAM for precise esophageal cancer gross target volume segmentation

Experimental results show that this segmentation method based on SAM achieves better segmentation performance than existing single-pass methods on both internal and external validation sets, and can provide a reliable efficient scheme for the formulation of precise radiotherapy plans for esophageal cancer.

MGTP-Seg: a mask guidance and text prompting network for gross tumor volume segmentation in esophageal cancer radiotherapy

This work demonstrates that MGTP-Seg not only provides an accurate, interpretable, and clinically relevant solution for automatic GTV delineation, but also offers a novel methodological framework to fuse spatial priors with semantic knowledge in medical image analysis.