Chaoyong Wu ^1,2 Jianxin Chen ² Yiheng Chen ^3,4 Xu Wang ⁵ Zhicheng Zhang ^6* Lu Zhou ^3,4*

• ¹ Shenzhen Hospital, Beijing University of Chinese Medicine, Shenzhen, China
• ² School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
• ³ Traditional Chinese Medicine (Zhong Jing) School, Henan University Of Chinese Medicine, Zhengzhou, Henan Province, China
• ⁴ Henan University of Chinese Medicine, Zhengzhou, Henan Province, China
• ⁵ School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
• ⁶ Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, Guangdong Province, China

Lung cancer is a predominant cause of cancer-related mortality worldwide, necessitating precise tumor segmentation of medical images for accurate diagnosis and treatment. However, the intrinsic complexity and variability of tumor morphology pose substantial challenges to segmentation tasks. To address this issue, we propose a multitask connected U-Net model with a teacher-student framework to enhance the effectiveness of lung tumor segmentation.The proposed model and framework integrate PET knowledge into the segmentation process, leveraging complementary information from both CT and PET modalities to improve segmentation performance. Additionally, we implemented a tumor area detection method to enhance tumor segmentation performance. In extensive experiments on four datasets, the average Dice coefficient of 0.56, obtained using our model, surpassed those of existing methods such as Segformer (0.51), Transformer (0.50), and UctransNet (0.43). These findings validate the efficacy of the proposed method in lung tumor segmentation tasks.