Cerebrovascular Network Segmentation of MRA Images With Deep Learning
TL
TL;DR
This work presents a convolutional neural network approach for segmentation of the cerebrovascular structure from magnetic resonance angiography inspired by the U-net 3D and by the Inception modules, entitled Uception.
Abstract
Deep learning has been shown to produce state of the art results in many tasks in biomedical imaging, especially in segmentation. Moreover, segmentation of the cerebrovascular structure from magnetic resonance angiography is a challenging problem because its complex geometry and topology have a large inter-patient variability. Therefore, in this work, we present a convolutional neural network approach for this problem inspired by the U-net 3D and by the Inception modules, entitled Uception. State of the art models are implemented for a comparison purpose and final results show that the proposed architecture has the best performance in this particular context.
References14 items
1
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes
2
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
3
Snapshot Ensembles: Train 1, get M for free
4
A survey on deep learning in medical image analysis
5