Summary of Perivascular Space Identification Nnunet For Generalised Usage (pingu), by Benjamin Sinclair et al.
Perivascular space Identification Nnunet for Generalised Usage (PINGU)
by Benjamin Sinclair, Lucy Vivash, Jasmine Moses, Miranda Lynch, William Pham, Karina Dorfman, Cassandra Marotta, Shaun Koh, Jacob Bunyamin, Ella Rowsthorn, Alex Jarema, Himashi Peiris, Zhaolin Chen, Sandy R Shultz, David K Wright, Dexiao Kong, Sharon L. Naismith, Terence J. OBrien, Meng Law
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a deep learning-based approach for segmenting perivascular spaces (PVSs) from magnetic resonance imaging (MRI) scans. The authors develop a neural network called nnUNet, trained on a diverse dataset of manually segmented MRI images with varying qualities and resolutions from six different datasets. They compare their model’s performance to publicly available deep learning methods for 3D PVS segmentation and demonstrate improved results, particularly in the basal ganglia region. The authors also explore the generalizability of their approach by training the model on data from unseen sites and find that it performs well even when applied to new datasets. This work presents a robust tool for identifying PVSs, which is essential for understanding brain function and disease. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about using computers to better understand how our brains work by analyzing special spaces in the brain called perivascular spaces. These spaces are important because they help remove waste from the brain. The authors created a new way to use computer algorithms, or “deep learning,” to analyze these spaces and compare it to other methods that have been tried before. They found that their method works better than others, especially in certain areas of the brain. This is important because understanding how these spaces work can help us diagnose and treat diseases like vascular disease. |
Keywords
» Artificial intelligence » Deep learning » Neural network
