A two-step nnDetection plus U-Net pipeline detects Circle of Willis bifurcation landmarks in MRA with lower mean radial error and fewer false positives than two heatmap-regression baselines.
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
This article presents a multiscale patch based convolutional neural network for the automatic segmentation of brain tumors in multi-modality 3D MR images. We use multiscale deep supervision and inputs to train a convolutional network. We evaluate the effectiveness of the proposed approach on the BRATS 2017 segmentation challenge where we obtained dice scores of 0.755, 0.900, 0.782 and 95% Hausdorff distance of 3.63mm, 4.10mm, and 6.81mm for enhanced tumor core, whole tumor and tumor core respectively.
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Two-Steps Neural Networks for an Automated Cerebrovascular Landmark Detection
A two-step nnDetection plus U-Net pipeline detects Circle of Willis bifurcation landmarks in MRA with lower mean radial error and fewer false positives than two heatmap-regression baselines.