ArticleFrontiers in radiology2025
Brain tumor segmentation using deep learning: high performance with minimized MRI data.
Article in Frontiers in radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A survey of deep learning techniques in detecting neurological disorders using MRI.Biomedical engineering online · 2026Pooled it
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Quantum-inspired optimization of transformer-capsule networks for accurate brain tumor segmentation and classification.Scientific reports · 2026Article
- 3D Medical Image Segmentation with 3D Modelling.Bioengineering (Basel, Switzerland) · 2026Article
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10 authors.
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Abstract
Purpose: Brain tumor segmentation with MRI is a challenging task, traditionally relying on manual delineation of regions-of-interest across multiple imaging sequences. However, this data-intensive approach is time-consuming. We aimed to optimize the process by using a deep learning (DL) based model while minimizing the number of MRI sequences required to segment gliomas. Methods: We trained a 3D U-Net DL model using the annotated 2018 MICCAI BraTS dataset (training dataset, Results: Dice scores on cross-validation showed that T1C + FLAIR (ET: 0.814, TC: 0.856) matched or outperformed those of T1 + T2 + T1C + FLAIR (ET: 0.785, TC: 0.841), T1C-only (ET: 0.781, TC: 0.852) and FLAIR-only (ET: 0.008, TC: 0.619). Results on the test dataset also showed that T1C + FLAIR (ET: 0.867, TC: 0.926) matched or outperformed those of T1 + T2 + T1C + FLAIR (ET: 0.835, TC: 0.908), T1C-only (ET: 0.726, TC: 0.928), and FLAIR-only (ET: 0.056, TC: 0.543). T1C + FLAIR excelled in both ET and TC, exceeding the performance of the four-sequence dataset. T1C-only matched T1C + FLAIR in TC performance. Similarly Conclusions: DL-based brain tumor segmentation can achieve high accuracy using only two MRI sequences (T1C + FLAIR). Reduction of multiple sequence dependency may enhance DL generalizability and dissemination in both clinical and research contexts. Our findings may ultimately help mitigate human labor intensity of a complex task integral to medical imaging analysis.
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