Summary of Building Brain Tumor Segmentation Networks with User-assisted Filter Estimation and Selection, by Matheus A. Cerqueira et al.
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection
by Matheus A. Cerqueira, Flávia Sprenger, Bernardo C. A. Teixeira, Alexandre X. Falcão
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper presents a novel approach to training deep-learning models for brain tumor image segmentation, addressing limitations in traditional methods. It introduces Multi-Step (MS) Feature Learning from Image Markers (FLIM), a user-assisted methodology that estimates and selects relevant filters from multiple FLIM executions. This approach is used only for the first convolutional layer, showing improved results over FLIM. The paper also proposes a simple U-shaped encoder-decoder network, sU-Net, for glioblastoma segmentation using T1Gd and FLAIR MRI scans, comparing its performance with State-Of-The-Art (SOTA) deep-learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Brain tumor image segmentation is a challenging problem in which deep-learning models have shown the best results. However, traditional methods of training these models from many pre-annotated images leave unanswered questions. A new approach called Multi-Step FLIM helps reduce human effort in data annotation and build models that are sufficiently deep for the problem at hand. This paper also presents a simple network for glioblastoma segmentation using MRI scans. |
Keywords
» Artificial intelligence » Deep learning » Encoder decoder » Image segmentation
