Summary of Seghed+: Segmentation Of Heterogeneous Data For Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation, by Berke Doga Basaran et al.
SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation
by Berke Doga Basaran, Paul M. Matthews, Wenjia Bai
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Machine learning models have shown promise in automating the segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance (MR) images. However, training these models typically requires large, well-annotated datasets, which are often limited in size and exhibit different formats and annotation styles. To address this issue, we introduce SegHeD+, a novel segmentation model that can handle multiple datasets and tasks, accommodating heterogeneous input data and performing segmentation for all lesions, new lesions, and vanishing lesions. The model incorporates longitudinal, anatomical, and volumetric constraints into the segmentation process. Additionally, lesion-level data augmentation is used to enlarge the training set and further improve segmentation performance. SegHeD+ is evaluated on five MS datasets and demonstrates superior performance in segmenting all, new, and vanishing lesions, surpassing several state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SegHeD+ is a new way to help doctors see MS lesions better. Right now, it’s hard to make computers see these lesions because there aren’t enough good pictures to train the computer. This makes it hard for computers to learn how to find all the different types of MS lesions. The people who made SegHeD+ came up with a solution by making the computer look at lots of different kinds of pictures and learn from them. They also added some special rules that help the computer understand what the pictures mean. This makes it way better at finding MS lesions than other computers are. |
Keywords
» Artificial intelligence » Data augmentation » Machine learning
