Summary of Ai Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-friendly Gui, by Ayman Al-kababji and Faycal Bensaali and Sarada Prasad Dakua and Yassine Himeur
AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI
by Ayman Al-Kababji, Faycal Bensaali, Sarada Prasad Dakua, Yassine Himeur
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The study uses convolutional neural networks (ConvNets) to segment liver tissues, developing a user-friendly graphical user interface (GUI) tool called “AI Radiologist” that enables clinicians to effectively delineate different liver tissues. The GUI is designed using the PyQt5 Python framework and allows for offline use, utilizing three ConvNet models trained to segment all liver tissues. The models achieved high scores on the Dice metric, with 98.16% for liver tissue, 65.95% for tumors, and 51.94% for vessels. The tool outputs 2D slices of liver, tumor, and vessel segments, along with 3D interpolations in .obj and .mtl formats, which can be visualized using any 3D-compatible software. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer programs called ConvNets to help doctors look at pictures of the liver. It also makes a tool that lets doctors use these programs without needing to know how they work. The tool is like a special kind of camera that can show doctors what’s going on inside the liver, and it’s really good at doing this job! Doctors can even print out 3D pictures of the liver to help them understand more about what they’re seeing. |
