SegmentationReview:A Slicer3D extension for fast review of AI-generated segmentations

SegmentationReview is a package developed in Python for fast review and editing of biomedical image segmentations. Biomedical imaging segmentation quality assessment is a crucial part of the development medical artificial intelligence (AI) algorithms but is time-consuming and labor-intensive. SegmentationReview has several components that facilitate efficient segmentation review, including automated importing of lists of images and segmentations into Slicer3D, a user-friendly graphical user interface for reviewing and assessing the quality of the segmentation, and automated tabular data-saving. The package has been tested and released as an open-source extension for Slicer3D. It enables fast, user-friendly review and editing for biomedical image segmentations

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning

PURPOSE: To develop and externally validate a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pLGG. MATERIALS AND METHODS: We conducted a retrospective study of two pLGG datasets with linked genomic and diagnostic T2-weighted MRI of patients: BCH (development dataset, n=214 [60 (28%) BRAF fusion, 50 (23%) BRAF V600E, 104 (49%) wild-type), and Child Brain Tumor Network (CBTN) (external validation, n=112 [60 (53%) BRAF-Fusion, 17 (15%) BRAF-V600E, 35 (32%) wild-type]). We developed a deep learning pipeline to classify BRAF mutational status (V600E vs. fusion vs. wild-type) via a two-stage process: 1) 3D tumor segmentation and extraction of axial tumor images, and 2) slice-wise, deep learning-based classification of mutational status. We investigated knowledge-transfer and self-supervised approaches to prevent model overfitting with a primary endpoint of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we developed a novel metric, COMDist, that quantifies the accuracy of model attention around the tumor. RESULTS: A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest macro-average AUC (0.82 [95% CI: 0.70-0.90]) and accuracy (77%) on internal validation, with an AUC improvement of +17.7% and a COMDist improvement of +6.4% versus training from scratch. On external validation, the TransferX model yielded AUC (0.73 [95% CI 0.68-0.88]) and accuracy (75%). CONCLUSION: Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pLGG mutational status prediction in a limited data scenario