Conceitos essenciais
Deep learning models for brain tumor segmentation can be enhanced through user-assisted filter estimation and selection, as demonstrated by the Multi-Step FLIM approach.
Resumo
The content discusses the development of Multi-Step (MS) FLIM, an innovative user-assisted method to estimate and select relevant filters for brain tumor segmentation networks. The article highlights the challenges in traditional deep-learning model training methods and introduces MS-FLIM as a solution to improve model performance. By involving users in filter estimation and selection, MS-FLIM enhances the effectiveness of brain tumor segmentation networks. The study compares different training methods using a U-shaped encoder-decoder network named sU-Net for glioblastoma segmentation. Results show that sU-Net based on MS-FLIM outperforms other techniques, achieving effectiveness comparable to State-Of-The-Art models within standard deviations.
1. Introduction
- Brain tumors are common in adults, requiring accurate diagnosis through MRI scans.
- Manual annotation for tumor segmentation is time-consuming, leading to research on automatic methods.
2. Related Work
- CNN-based models like U-Net and DeepMedic are widely used for brain tumor segmentation.
3. The Proposed MS-FLIM
- MS-FLIM involves user intervention in selecting relevant filters for the initial convolutional layers.
4. Experimental Setup
- A shallow U-shaped encoder-decoder network called sU-Net was utilized for GBM tumor segmentation.
5. Results and Discussion
- Comparison of sU-Nets with SOTA models like DeepMedic and nnU-net shows promising results with MS-FLIM.
6. Conclusion
- MS-FILM offers an improvement over traditional methodologies by enhancing filter selection accuracy.
Estatísticas
In this work, we present Multi-Step (MS) FLIM – a user-assisted approach to estimating and selecting the most relevant filters from multiple FLIM executions.
For evaluation, we build a simple U-shaped encoder-decoder network, named sU-Net, for glioblastoma segmentation using T1Gd and FLAIR MRI scans.
The results show that the sU-Net based on MS-FILM outperforms other training methods and achieved effectiveness within the standard deviations of the SOTA models.
Citações
"MS-FILM involves user intervention in selecting relevant filters for the initial convolutional layers."
"Results show that sU-NET based on MS-FILM outperforms other techniques."