祝贺本小组24级研究生陈畅同学的论文被中科院2区SCI期刊Scientific Data录用！

祝贺本小组24级研究生陈畅同学的论文Computed tomography lung tumor dataset for deep learning to facilitate medical image segmentation被中科院2区SCI期刊Scientific Data录用！

   该论文是本科研团队与安徽省胸科医院合作的第一个成果！在本团队周昊老师与安徽省胸科医院宋奇隆医生的共同指导下完成。论文提出了一个肺部肿瘤数据集与图像分割模型，为肺部肿瘤分割做出了一定的贡献。

论文摘要：

In-depth analysis of the morphological characteristics of lung tumors in computed tomography (CT) images is crucial for achieving accurate tumor segmentation. While deep learning models based on artificial intelligence have demonstrated excellent segmentation performance, the lack of high-quality training data limits their reliability and applicability in this field. This study constructed a CT image dataset for lung tumor segmentation, comprising 730 cases of early-stage lung cancer and 449 cases of advanced-stage lung cancer. The data were segmented by experienced radiologists and radiation oncologists, and the patient information was anonymized. We proposed a segmentation model based on Segment Anything Model 2 (SAM2) with adapter fine-tuning and contrast-awareness. The Dice coefficients on the proposed dataset reached 91.64% and 89.51%, respectively, demonstrating the feasibility of training the segmentation model using the constructed dataset. This dataset can be used for the development, comparison, and validation of lung tumor CT image segmentation methods, contributing to the promotion of reproducible research and application exploration in the field of medical image segmentation.

论文链接：https://doi.org/10.1038/s41597-026-07963-0