Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation Article Swipe
Zhifan Jiang
,
Daniel Capellán-Martín
,
Abhijeet Parida
,
Austin Tapp
,
Xinyang Liu
,
María J. Ledesma‐Carbayo
,
Syed Muhammad Anwar
,
Marius George Linguraru
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.04094
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.04094
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 https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids. Additionally, an open-source web-application is accessible at https://segmenter.hope4kids.io/ which uses the docker container aparida12/brats-peds-2024:v20240913 .
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.04094
- https://arxiv.org/pdf/2412.04094
- OA Status
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- Related Works
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- OpenAlex ID
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https://openalex.org/W4405095149Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.04094Digital Object Identifier
- Title
-
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor SegmentationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-05Full publication date if available
- Authors
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Zhifan Jiang, Daniel Capellán-Martín, Abhijeet Parida, Austin Tapp, Xinyang Liu, María J. Ledesma‐Carbayo, Syed Muhammad Anwar, Marius George LinguraruList of authors in order
- Landing page
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https://arxiv.org/abs/2412.04094Publisher landing page
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https://arxiv.org/pdf/2412.04094Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2412.04094Direct OA link when available
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Subtyping, Feature (linguistics), Segmentation, Magnetic resonance imaging, Computer science, Artificial intelligence, Neuroimaging, Pattern recognition (psychology), Nuclear magnetic resonance, Physics, Psychology, Neuroscience, Medicine, Radiology, Philosophy, Linguistics, Programming languageTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.which | 18, 206 |
| abstract_inverted_index.whole | 161 |
| abstract_inverted_index.(MET), | 65 |
| abstract_inverted_index.(PED), | 59 |
| abstract_inverted_index.0.801, | 156 |
| abstract_inverted_index.0.926, | 155 |
| abstract_inverted_index.docker | 209 |
| abstract_inverted_index.employ | 111 |
| abstract_inverted_index.method | 147 |
| abstract_inverted_index.nature | 122 |
| abstract_inverted_index.offers | 37 |
| abstract_inverted_index.source | 189 |
| abstract_inverted_index.tumors | 6, 47, 58, 125 |
| abstract_inverted_index.types. | 187 |
| abstract_inverted_index.unique | 39 |
| abstract_inverted_index.(BraTS) | 34 |
| abstract_inverted_index.(mpMRI) | 12 |
| abstract_inverted_index.MEN-RT, | 165 |
| abstract_inverted_index.changes | 76 |
| abstract_inverted_index.imaging | 11 |
| abstract_inverted_index.models. | 100, 140 |
| abstract_inverted_index.others. | 67 |
| abstract_inverted_index.present | 126 |
| abstract_inverted_index.propose | 90 |
| abstract_inverted_index.refined | 84 |
| abstract_inverted_index.regions | 86 |
| abstract_inverted_index.results | 170 |
| abstract_inverted_index.testing | 144 |
| abstract_inverted_index.various | 43, 184 |
| abstract_inverted_index.(MEN-RT) | 61 |
| abstract_inverted_index.Accurate | 0 |
| abstract_inverted_index.Compared | 68 |
| abstract_inverted_index.achieved | 148 |
| abstract_inverted_index.adaptive | 105 |
| abstract_inverted_index.analyses | 114 |
| abstract_inverted_index.approach | 95, 132, 176 |
| abstract_inverted_index.clinical | 25, 80 |
| abstract_inverted_index.enhances | 133 |
| abstract_inverted_index.ensemble | 94 |
| abstract_inverted_index.increase | 79 |
| abstract_inverted_index.magnetic | 9 |
| abstract_inverted_index.previous | 70 |
| abstract_inverted_index.radiomic | 113 |
| abstract_inverted_index.Challenge | 35 |
| abstract_inverted_index.MRI-based | 112 |
| abstract_inverted_index.automatic | 2 |
| abstract_inverted_index.available | 195 |
| abstract_inverted_index.container | 210 |
| abstract_inverted_index.datasets, | 130 |
| abstract_inverted_index.diagnosis | 26 |
| abstract_inverted_index.editions, | 71 |
| abstract_inverted_index.essential | 14 |
| abstract_inverted_index.important | 22 |
| abstract_inverted_index.improving | 178 |
| abstract_inverted_index.including | 42 |
| abstract_inverted_index.introduce | 103 |
| abstract_inverted_index.pediatric | 52, 56 |
| abstract_inverted_index.precision | 135 |
| abstract_inverted_index.resonance | 10 |
| abstract_inverted_index.subtypes. | 118 |
| abstract_inverted_index.accessible | 203 |
| abstract_inverted_index.integrates | 97 |
| abstract_inverted_index.metastases | 64 |
| abstract_inverted_index.prognosis. | 28 |
| abstract_inverted_index.relevance, | 81 |
| abstract_inverted_index.similarity | 152 |
| abstract_inverted_index.techniques | 109 |
| abstract_inverted_index.demonstrate | 171 |
| abstract_inverted_index.evaluation. | 88 |
| abstract_inverted_index.implemented | 75 |
| abstract_inverted_index.innovative, | 104 |
| abstract_inverted_index.lesion-wise | 150 |
| abstract_inverted_index.meningiomas | 60 |
| abstract_inverted_index.open-source | 200 |
| abstract_inverted_index.performance | 180 |
| abstract_inverted_index.Segmentation | 33 |
| abstract_inverted_index.benchmarking | 40 |
| abstract_inverted_index.coefficients | 153 |
| abstract_inverted_index.increasingly | 21 |
| abstract_inverted_index.opportunity, | 41 |
| abstract_inverted_index.populations, | 53 |
| abstract_inverted_index.quantitative | 16 |
| abstract_inverted_index.segmentation | 3, 99, 139, 179 |
| abstract_inverted_index.Additionally, | 101, 198 |
| abstract_inverted_index.International | 30 |
| abstract_inverted_index.differentiate | 116 |
| abstract_inverted_index.effectiveness | 173 |
| abstract_inverted_index.heterogeneous | 121 |
| abstract_inverted_index.measurements, | 17 |
| abstract_inverted_index.respectively. | 168 |
| abstract_inverted_index.substantially | 78 |
| abstract_inverted_index.implementation | 193 |
| abstract_inverted_index.learning-based | 93 |
| abstract_inverted_index.post-processing | 108 |
| abstract_inverted_index.web-application | 201 |
| abstract_inverted_index.generalizability | 137, 182 |
| abstract_inverted_index.multi-parametric | 8 |
| abstract_inverted_index.state-of-the-art | 98 |
| abstract_inverted_index.https://segmenter.hope4kids.io/ | 205 |
| abstract_inverted_index.aparida12/brats-peds-2024:v20240913 | 211 |
| abstract_inverted_index.https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids. | 197 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |