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Found 6 papers, 1 papers with code
The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization
We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation.
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives
Even with a simulated false negative rate as high as 50%, applying our loss function to randomly censored data preserves maximum sensitivity at 97% of the baseline with uncensored training data, compared to just 10% for a standard loss function.
Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study
A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients.
MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation
We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences.
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI
For an optimal probability threshold, detection and segmentation performance was assessed on a per metastasis basis.
