Self-supervised low-dose Computed Tomography (LDCT) imaging methods have demonstrated significant clinical potential as they can train an efficient denoising model without high-quality normal-dose CT (NDCT) images. However, existing methods only focus on improving overall quality of the images, potentially resulting in the loss of details in critical areas when subjected to high levels of noise. To address this issue, we develop a mask guided network to enhance the quality of the desired regions in a self-supervised manner. Firstly, an adaptive organ segmentation model is trained with efficient fine-tuning strategies based on the Segment Anything Model. Secondly, we utilize the proposed segmentation model to generate mask embeddings for each LDCT image, incorporating both positional information of the target (e.g., liver and kidney) and latent image features. Finally, we propose a novel noise reduction network that incorporates mask embedding into self-supervised learning to recover high-quality CT images. Comprehensive comparisons and analyses on two datasets have demonstrated that the proposed method can achieve excellent performance in suppressing overall noise and improve imaging quality in key areas.