Detecting the left atrial appendage in CT localizers using deep learning - Aydin Demircioğlu; Denise Bos; Anton S. Quinsten; Lale Umutlu; Oliver Bruder; Michael Forsting; Kai Nassenstein (Scientific Reports)
Abstract Patients with cardioembolic stroke often undergo CT of the left atrial appendage (LAA), for example, to determine whether thrombi are present in the LAA. To guide the imaging process, technologists first perform a localizer scan, which is a preliminary image used to identify the region of interest. However, the lack of well-defined landmarks makes accurate delimitation of the LAA in localizers difficult and often requires whole-heart scans, increasing radiation exposure and cancer risk. This study aims to automate LAA delimitation in CT localizers using deep learning. Four commonly used deep networks (VariFocalNet, Cascade-R-CNN, Task-aligned One-stage Object Detection Network, YOLO v11) were trained to predict the LAA boundaries on a cohort of 1253 localizers, collected retrospectively from a single center. The best-performing network in terms of delimitation accuracy was then evaluated on an internal test cohort of 368 patients, and on an external test cohort of 309 patients. The VariFocalNet performed best, achieving LAA delimitations with high accuracy (97.8% and 96.8%; Dice coefficients: 90.4% and 90.0%) and near-perfect clinical utility (99.8% and 99.3%). Compared to whole-heart scanning, the network-based delimitation reduced the radiation exposure by more than 50% (5.33 ± 6.42 mSv vs. 11.35 ± 8.17 mSv in the internal cohort, 4.39 ± 4.23 mSv vs. 10.09 ± 8.0 mSv in the external cohort). This study demonstrates that a deep learning network can accurately delimit the LAA in the localizer, leading to more accurate CT scans of the LAA, thereby significantly reducing radiation exposure to the patient compared to whole-heart scanning.