Tuberculosis (TB) is one of the leading causes of death worldwide. Automated detection of lung caverns associated with TB in Computed Tomography (CT) could help clinicians optimise treatment. However, caverns detection on 3D CT data is challenging due to the curse of dimensionality, thus requiring larger training data and more computational resource. Our team (AEHRC CSIRO) participated in ImageCLEFmed TB caverns detection 2022 to address this challenge, by developing a 2D YOLO-based model (TBdet-2D), and an efficient 3D Retina-U-Net-based model (TBdet-3D). Both networks were trained on 559 CT data with data augmentation and tested on 140 data provided by the challenge. The results show that TBdet-3D (mAP_IoU 0.504) outperformed TBdet-2D model (mAP_IoU 0.308) on testing data, indicating that employing a 3D approach instead of a 2D approach is more appropriate for the task. Our team placed first among the participating teams in this challenge. An overview of ImageCLEFmed Tuberculosis 2022 is available at: https://www.imageclef.org/2022/medical/tuberculosis.