bibtext: @InProceedings{ Nae_Trexplorer_MICCAI2024,
                   author = { Naeem, Roman and Hagerman, David and Svensson, Lennart and Kahl, Fredrik },
                   title = { { Trexplorer: Recurrent DETR for Topologically Correct Tree Centerline Tracking } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: Tubular structures with tree topology such as blood vessels, lung airways, and more are abundant in human anatomy. Tracking these structures with correct topology is crucial for many downstream tasks that help in early detection of conditions such as vascular and pulmonary diseases. Current methods for centerline tracking suffer from predicting topologically incorrect centerlines and complex model pipelines. To mitigate these issues we propose Trexplorer, a recurrent DETR based model that tracks topologically correct centerlines of tubular tree objects in 3D volumes using a simple model pipeline. We demonstrate the model’s performance on a publicly available synthetic vessel centerline dataset and show that our model outperforms the state-of-the-art on centerline topology and graph-related metrics, and performs well on detection metrics. The code is available at https://github.com/RomStriker/Trexplorer.
title: Trexplorer: Recurrent DETR for Topologically Correct Tree Centerline Tracking
authors: Naeem, Roman Hagerman, David Svensson, Lennart Kahl, Fredrik