ArachNet: Interpretable Sub-Arachnoid Space Segmentation Using an Additive Convolutional Neural Network
Author:
Affiliation:
1. University of Thessaly,School of Science,Dept. of Computer Science and Biomedical Informatics
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635787.pdf?arnumber=10635787
Reference24 articles.
1. Biomedical applications of soft robotics
2. SEGMENTATION OF SPINAL SUBARACHNOID LUMEN WITH 3D ATTENTION U-NET
3. A 3D subject-specific model of the spinal subarachnoid space with anatomically realistic ventral and dorsal spinal cord nerve rootlets
4. Anthropomorphic Model of Intrathecal Cerebrospinal Fluid Dynamics Within the Spinal Subarachnoid Space: Spinal Cord Nerve Roots Increase Steady-Streaming
5. Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus—transfer learning from existing algorithms
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