Electrical Engineering and Systems Science > Image and Video Processing
[Submitted on 5 Dec 2024 (v1), last revised 20 Dec 2024 (this version, v3)]
Title:Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation
View PDF HTML (experimental)Abstract:Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly important role in clinical diagnosis and prognosis. The International Brain Tumor Segmentation (BraTS) Challenge 2024 offers a unique benchmarking opportunity, including various types of brain tumors in both adult and pediatric populations, such as pediatric brain tumors (PED), meningiomas (MEN-RT) and brain metastases (MET), among others. Compared to previous editions, BraTS 2024 has implemented changes to substantially increase clinical relevance, such as refined tumor regions for evaluation. We propose a deep learning-based ensemble approach that integrates state-of-the-art segmentation models. Additionally, we introduce innovative, adaptive pre- and post-processing techniques that employ MRI-based radiomic analyses to differentiate tumor subtypes. Given the heterogeneous nature of the tumors present in the BraTS datasets, this approach enhances the precision and generalizability of segmentation models. On the final testing sets, our method achieved mean lesion-wise Dice similarity coefficients of 0.926, 0.801, and 0.688 for the whole tumor in PED, MEN-RT, and MET, respectively. These results demonstrate the effectiveness of our approach in improving segmentation performance and generalizability for various brain tumor types.
The source code of our implementation is available at this https URL. Additionally, an open-source web-application is accessible at this https URL which uses the docker container aparida12/brats-peds-2024:v20240913 .
Submission history
From: Abhijeet Parida [view email][v1] Thu, 5 Dec 2024 12:00:00 UTC (5,585 KB)
[v2] Thu, 12 Dec 2024 22:13:21 UTC (5,585 KB)
[v3] Fri, 20 Dec 2024 22:37:27 UTC (5,585 KB)
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