The status of mediastinal lymph nodes plays an important role in accurate clinical staging, treatment selection and prognosis improvement of cancer patients. However, the contrast between lymph nodes and surrounding tissues in computed tomography (CT) images is low, and the size of lymph nodes varies, making manual identification and statistics of lymph nodes time-consuming and inefficient. Moreover, there are few research on instance segmentation of 3D volumetric image. In this paper, we propose a novel 3D image instance segmentation framework called InsSegLN and establish a benchmark for the challenging mediastinal lymph node instance segmentation task. InsSegLN is the first end-to-end 3D instance segmentation model directly processing 3D volumetric data for lymph nodes. It divides the instance segmentation task into a segmentation subtask and a detection subtask, and obtains the instance segmentation result by integrating results of the two subtasks. In order to solve problems such as blurred edges and large size differences of lymph nodes, we build a new backbone and make improvements on the feature pyramid and detection heads. And we use border-core representations to supervise training, which is helpful for the model to identify touched lymph node individuals. Finally, we provide a simple but effective method to integrate the detection result with the segmentation result. We conducted our experiments on a public dataset and an inhouse dataset of mediastinal lymph node and validate the effectiveness of our improvement measures through ablation study. Compared with the baseline method, on the public dataset, InsSegLN improves AP from 0.1971 to 0.2605 when the IoU threshold is set to 0.5, and improves mAP from 0.0764 to 0.1269 when the IoU threshold ranges from 0.5 to 0.9. InsSegLN also achieves significant performance improvement on our inhouse dataset, showing the effectiveness of our method.