Summary of Towards Synergistic Deep Learning Models For Volumetric Cirrhotic Liver Segmentation in Mris, by Vandan Gorade et al.
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
by Vandan Gorade, Onkar Susladkar, Gorkem Durak, Elif Keles, Ertugrul Aktas, Timurhan Cebeci, Alpay Medetalibeyoglu, Daniela Ladner, Debesh Jha, Ulas Bagci
First submitted to arxiv on: 8 Aug 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 novel approach for precise segmentation of liver ROIs in magnetic resonance imaging (MRI) scans, which is crucial for monitoring and treatment planning in liver cirrhosis patients. The proposed architecture, nnSynergyNet3D, leverages complementary latent spaces to capture complex feature interactions, enabling effective modeling of intricate relationships between features. This approach outperforms existing models on a private dataset of 628 high-resolution T1 abdominal MRI scans from 339 patients, with an approximate 2% improvement over the baseline model, nnUNet3D. Additionally, zero-shot testing on healthy liver CT scans from the public LiTS dataset demonstrates superior cross-modal generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Liver cirrhosis is a major cause of global mortality, and precise segmentation of ROIs in MRI scans is crucial for monitoring and treatment planning. A new approach called nnSynergyNet3D helps capture complex feature interactions to improve segmentation accuracy. The model works better than existing models on a dataset of MRI scans from patients with liver cirrhosis. |
Keywords
» Artificial intelligence » Generalization » Zero shot
