atTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning-Reference-Cited by-同舟云学术

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atTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning

Published:2023 Issue: Volume: Page:237-246
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Peretzke Robin,Maier-Hein Klaus H.,Bohn Jonas,Kirchhoff Yannick,Roy Saikat,Oberli-Palma Sabrina,Becker Daniela,Lenga Pavlina,Neher Peter

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-43993-3_23

Reference21 articles.

1. Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44(4), 625–632 (2000)

2. Berman, J.: Diffusion MR tractography as a tool for surgical planning. Magn. Reson. Imaging Clin. N. Am. 17(2), 205–214 (2009)

3. Bertò, G., et al.: Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation. Neuroimage 224, 117402 (2021)

4. Garyfallidis, E., Brett, M., Correia, M.M., Williams, G.B., Nimmo-Smith, I.: QuickBundles, a method for tractography simplification. Front. Neurosci. 6, 175 (2012)

5. Garyfallidis, E., Ocegueda, O., Wassermann, D., Descoteaux, M.: Robust and efficient linear registration of white-matter fascicles in the space of streamlines. Neuroimage 117, 124–140 (2015)

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep intravital brain tumor imaging enabled by tailored three-photon microscopy and analysis;Nature Communications;2024-09-10

2. A systematic review of automated methods to perform white matter tract segmentation;Frontiers in Neuroscience;2024-03-19

3. Abstract: Semi-automatic White Matter Tract Segmentation using Active Learning atTRACTive;Informatik aktuell;2024

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