TotalSegmentator MRI is an open-source, user-friendly tool that provides automatic, robust sequence-independent segmentations of major anatomic structures on MRI scans; the ready-to-use online tool is available at https://totalsegmentator.com .

This study aims to develop a tool based on deep learning algorithms for automatic liver segment and liver lesion segmentation on Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). We demonstrate its clinical utility using a qualitative example of hepatocellular carcinoma (HCC) response to transarterial chemoembolization (TACE). The models are provided as open-source software to update and improve the capabilities of TotalSegmentator. Liver segmentation was performed on 193 CTs and 120 MRIs, using fivefold cross-validation for training/testing. 429 CTs and 321 MRIs with liver lesions and 15 CTs and 13 MRIs without liver lesions were collected. Of these 414 CTs and 308 MRIs were manually segmented and a nnU-Net was trained on 750 images and tested on 80. Inter-rater variability was examined on 20 CTs and 20 MRIs by two independent readers. We analyzed its potential clinical utility on 172 TACE-treated HCC on CT. Performance was evaluated using sensitivity, false positives, and volume. Voxel-wise segmentation was evaluated using the Dice coefficient. Our model’s liver segmentation achieved Dice coefficients of 0.897 for CT and 0.847 for MRI. Liver lesion detection on CT achieved 75.8% sensitivity, 0.522 false positives per case (FP/c), and 0.658 Dice; on MRI, 62.7% sensitivity, 1.029 FP/c, and 0.337 Dice. Following TACE, median HCC attenuation significantly decreased from 51.33 HU to 38.5 HU. Human readers showed higher agreement (sensitivity: 64.7%, Dice: 0.464) than Lesion model (LM)-reader comparisons (sensitivity: 53.2%, Dice: 0.432) and the LM had a slightly higher FP/c (0.825 vs. 0.775). Overall, our algorithms reliably detect and segment liver segments and lesions on both CT and MRI and the qualitative assessment of HCC response to TACE illustrates the model’s potential value for clinical and research applications.

The aim of this study was to develop an open-source nnU-Net-based AI model for combined detection and segmentation of unruptured intracranial aneurysms (UICA) in 3D TOF-MRI and compare models trained on datasets with aneurysm-like differential diagnoses. This retrospective study (2020–2023) included 385 anonymized 3D TOF-MRI images from 345 patients (mean age 59 years, 60% female) at multiple centers plus 113 subjects from the ADAM challenge. Images featured untreated or possible UICA and differential diagnoses. Four distinct training datasets were created, and the nnU-Net framework was used for model development. Performance was assessed on a separate test set using sensitivity and false positive (FP)/case rate for detection and DICE score and NSD (normalized surface distance, 0.5 mm threshold) for segmentation. Segmentation performance on the test set was also compared to a second human reader. The four models achieved overall sensitivity between 82 and 85% and an FP/case rate of 0.20 to 0.31, with no significant differences ( p = 0.90 and p = 0.16) between them. The primary model showed 85% sensitivity and 0.23 FP/case rate, outperforming the ADAM-challenge winner (61%) and a nnU-Net trained on ADAM data (51%) in sensitivity ( p < 0.05). Mean DICE (0.73) and NSD (0.84 for 0.5 mm threshold) for correctly detected UICA did not significantly differ from human reader performance. Our open-source, nnU-Net-based AI model (available at https://zenodo.org/records/13386859 ) demonstrates high sensitivity, low FP rates, and consistent segmentation accuracy for UICA detection and segmentation in 3D TOF-MRI, suggesting its potential to improve clinical diagnosis and monitoring of UICA.