Bag of Tricks for 3D MRI Brain Tumor Segmentation

Zhao, Yuanxing; Zhang, Yanming; Liu, Cheng‐Lin

doi:10.1007/978-3-030-46640-4_20

Cited by 104 publications

(58 citation statements)

References 9 publications

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“…It can help in early diagnosis as well as in therapeutic strategy planning. In recent years, deep learning-based segmentation approaches have outperformed traditional state-of-the-art methods [3,4]. Segmentation delineates the brain tumor into Whole Tumor (WT), Enhancing Tumor (ET), and Tumor Core (TC).…”

Section: Introductionmentioning

confidence: 99%

Glioblastoma Multiforme Patient Survival Prediction

Rajput¹,

Agravat²,

Roy³

et al. 2021

Preprint

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Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image features with random forest regressor approach was 51.5\% for the training and 51.7\% for the validation dataset. The gradient boosting regressor with shape features gave an accuracy of 91.5\% and 62.1\% on training and validation datasets respectively. It is better than the BraTS 2020 survival prediction challenge winners on the training and validation datasets. Our work shows that handcrafted features exhibit a strong correlation with survival prediction. The consensus based regressor with gradient boosting and radiomics shape features is the best combination for survival prediction.

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Section: Introductionmentioning

confidence: 99%

Glioblastoma Multiforme Patient Survival Prediction

Rajput¹,

Agravat²,

Roy³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The cascaded U-Net employed by Jiang et al [51] achieved the best scores of the challenge, to which our results compare favourably, with significant performance gap occurring in terms of the enhancing tumour. Our ensemble gives improved results for the tumour core than the DCNN used by Zhao et al [52] and just falls short for the enhancing tumour with a minor performance gap. Similarly, it segments the tumour core with more accuracy as compared to CNN developed by McKinley et al [53].…”

Section: A Comparison With Challenge Participantsmentioning

confidence: 74%

Brain Tumour Image Segmentation Using Deep Networks

et al. 2020

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Automated segmentation of brain tumour from multimodal MR images is pivotal for the analysis and monitoring of disease progression. As gliomas are malignant and heterogeneous, efficient and accurate segmentation techniques are used for the successful delineation of tumours into intra-tumoural classes. Deep learning algorithms outperform on tasks of semantic segmentation as opposed to the more conventional, context-based computer vision approaches. Extensively used for biomedical image segmentation, Convolutional Neural Networks have significantly improved the state-of-the-art accuracy on the task of brain tumour segmentation. In this paper, we propose an ensemble of two segmentation networks: a 3D CNN and a U-Net, in a significant yet straightforward combinative technique that results in better and accurate predictions. Both models were trained separately on the BraTS-19 challenge dataset and evaluated to yield segmentation maps which considerably differed from each other in terms of segmented tumour sub-regions and were ensembled variably to achieve the final prediction. The suggested ensemble achieved dice scores of 0.750, 0.906 and 0.846 for enhancing tumour, whole tumour, and tumour core, respectively, on the validation set, performing favourably in comparison to the state-of-the-art architectures currently available.

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“…Table 4 shows a comparative performance between the proposed AE AU-Net model and the two best-performing networks presented to BraTS 2019 ( 15 , 22 ). It can be observed in Table 4 that our method performs closely, although a little less, to the second-ranked submission in the validation dataset and that our model performs better than the second-place model in the enhancing tumor region.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Asymmetric Ensemble of Asymmetric U-Net Models for Brain Tumor Segmentation With Uncertainty Estimation

et al. 2021

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Accurate brain tumor segmentation is crucial for clinical assessment, follow-up, and subsequent treatment of gliomas. While convolutional neural networks (CNN) have become state of the art in this task, most proposed models either use 2D architectures ignoring 3D contextual information or 3D models requiring large memory capacity and extensive learning databases. In this study, an ensemble of two kinds of U-Net-like models based on both 3D and 2.5D convolutions is proposed to segment multimodal magnetic resonance images (MRI). The 3D model uses concatenated data in a modified U-Net architecture. In contrast, the 2.5D model is based on a multi-input strategy to extract low-level features from each modality independently and on a new 2.5D Multi-View Inception block that aims to merge features from different views of a 3D image aggregating multi-scale features. The Asymmetric Ensemble of Asymmetric U-Net (AE AU-Net) based on both is designed to find a balance between increasing multi-scale and 3D contextual information extraction and keeping memory consumption low. Experiments on 2019 dataset show that our model improves enhancing tumor sub-region segmentation. Overall, performance is comparable with state-of-the-art results, although with less learning data or memory requirements. In addition, we provide voxel-wise and structure-wise uncertainties of the segmentation results, and we have established qualitative and quantitative relationships between uncertainty and prediction errors. Dice similarity coefficient for the whole tumor, tumor core, and tumor enhancing regions on BraTS 2019 validation dataset were 0.902, 0.815, and 0.773. We also applied our method in BraTS 2018 with corresponding Dice score values of 0.908, 0.838, and 0.800.

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Bag of Tricks for 3D MRI Brain Tumor Segmentation

Cited by 104 publications

References 9 publications

Glioblastoma Multiforme Patient Survival Prediction

Glioblastoma Multiforme Patient Survival Prediction

Brain Tumour Image Segmentation Using Deep Networks

Asymmetric Ensemble of Asymmetric U-Net Models for Brain Tumor Segmentation With Uncertainty Estimation

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