Multi-centric AI Model for Unruptured Intracranial Aneurysm Detection and Volumetric Segmentation in 3D TOF-MRI

Purpose: 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. Methods: This retrospective study (2020-2023) included 385 anonymized 3D TOF-MRI images from 364 patients (mean age 59 years, 60% female) at multiple centers plus 113 subjects from the ADAM challenge. Images featured untreated or possible UICAs 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) with a 0.5mm threshold for segmentation. Statistical analysis included chi-square, Mann-Whitney-U, and Kruskal-Wallis tests, with significance set at p < 0.05. Results: Models achieved overall sensitivity between 82% and 85% and a FP/case rate of 0.20 to 0.31, with no significant differences (p = 0.90 and p = 0.16). 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). It achieved a mean DICE score of 0.73 and an NSD of 0.84 for correctly detected UICA. Conclusions: Our open-source, nnU-Net-based AI model (available at 10.5281/zenodo.13386859) demonstrates high sensitivity, low false positive rates, and consistent segmentation accuracy for UICA detection and segmentation in 3D TOF-MRI, suggesting its potential to improve clinical diagnosis and for monitoring of UICA.

Improved Segmentation and Detection Sensitivity of Diffusion-Weighted Brain Infarct Lesions with Synthetically Enhanced Deep Learning

Purpose: To compare the segmentation and detection performance of a deep learning model trained on a database of human-labelled clinical diffusion-weighted (DW) stroke lesions to a model trained on the same database enhanced with synthetic DW stroke lesions. Methods: In this institutional review board approved study, a stroke database of 962 cases (mean age 65+/-17 years, 255 males, 449 scans with DW positive stroke lesions) and a normal database of 2,027 patients (mean age 38+/-24 years,1088 females) were obtained. Brain volumes with synthetic DW stroke lesions were produced by warping the relative signal increase of real strokes to normal brain volumes. A generic 3D U-Net was trained on four different databases to generate four different models: (a) 375 neuroradiologist-labeled clinical DW positive stroke cases(CDB);(b) 2,000 synthetic cases(S2DB);(c) CDB+2,000 synthetic cases(CS2DB); or (d) CDB+40,000 synthetic cases(CS40DB). The models were tested on 20%(n=192) of the cases of the stroke database, which were excluded from the training set. Segmentation accuracy was characterized using Dice score and lesion volume of the stroke segmentation, and statistical significance was tested using a paired, two-tailed, Student's t-test. Detection sensitivity and specificity was compared to three neuroradiologists. Results: The performance of the 3D U-Net model trained on the CS40DB(mean Dice 0.72) was better than models trained on the CS2DB (0.70,P <0.001) or the CDB(0.65,P<0.001). The deep learning model was also more sensitive (91%[89%-93%]) than each of the three human readers(84%[81%-87%],78%[75%-81%],and 79%[76%-82%]), but less specific(75%[72%-78%] vs for the three human readers (96%[94%-97%],92%[90%-94%] and 89%[86%-91%]). Conclusion: Deep learning training for segmentation and detection of DW stroke lesions was significantly improved by enhancing the training set with synthetic lesions.