Although the application of supervised deep learning in medical image analysis is still very successful, it mainly depends on the quantity and quality of labeled data; and it is time-consuming and labor-intensive to obtain 3D medical image annotation. Recently, contrastive learning has shown its remarkable ability for self-supervised learning and has achieved impressive results on many downstream tasks. In this study, we extend the popular contrastive learning medical image segmentation framework to 3D and design extra reconstruction loss for volumetric medical images to improve the performance of global contrastive learning. We evaluate our method on two public 3D medical image datasets of different modalities. Our proposed method achieves competitive results compared to other methods for different proportions of labeled data.