Weakly supervised perivascular spaces segmentation with salient guidance of Frangi filter

To develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter-based image processing algorithm and the convolutional neural network. We present a weakly supervised learning method for PVS segmentation by combing a rule-based image processing approach Frangi...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Magnetic resonance in medicine Ročník 89; číslo 6; s. 2419 - 2431
Hlavní autori:	Lan, Haoyu, Lynch, Kirsten M., Custer, Rachel, Shih, Nien‐Chu, Sherlock, Patrick, Toga, Arthur W., Sepehrband, Farshid, Choupan, Jeiran
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Wiley Subscription Services, Inc 01.06.2023
Predmet:	Algorithms Artificial neural networks Conditional random fields Deep learning Entropy (Information theory) Field theory Humans Image filters Image processing Image Processing, Computer-Assisted - methods Image segmentation Machine learning Magnetic Resonance Imaging - methods Model accuracy Neural networks Neural Networks, Computer Optimization Substantia alba Supervised learning Training conditional random field image processing perivascular spaces structural MRI convolutional neural network
ISSN:	0740-3194, 1522-2594, 1522-2594
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!