MRI Brain Tumor Segmentation Using a 2D-3D U-Net Ensemble

Asenjo, J. Martí; Solís, Alfonso Martinez-Larraz

doi:10.1007/978-3-030-72084-1_32

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…For this purpose, multiple radiomic and image-based features were extracted from MRI volumes and segmented sub-regions. In [27], four networks -three 2D networks for each patient plane (axial, sagittal, and coronal) and one 3D networkwere combined to segment tumors from MRI images, with Dice scores of 0.75 for the enhancing tumor (ET), 0.81 for the whole tumor (WT), and 0.78 for the tumor core (TC). Using features extracted from the automatic segmentation, a survival prediction model was developed on Matlab, with gross tumor size and location being the major factors influencing survival prediction.…”

Section: Related Workmentioning

confidence: 99%

“…Table 2 shows the comparisons of the SpearmanR and Accuracy performances with other published methods on the BraTS 2020 validation dataset, while Table 2 presents a comparison of the SpearmanR performances with other published methods on the BraTS 2019 and BraTS 2018 validation datasets. From the experimental results, the FLAIR modality showed the highest correlation with the survival prediction output with a correlation SpearmanR of Method SpearmanR Accuracy [24] 0.123 0.345 [26] 0.134 0.483 [27] 0.134 0.517 [28] 0.217 0.517 [29] 0.228 0.414 [30] 0.249 0.517 [25] 0.253 0.414 [31] 0.280 0.450 Ours 0.459 0.517 5 A and Figure 5 B present comparisons of SpearmanR and Accuracy performances between using our FLAIR modality and other modalities, combination approaches on the BraTS 2020 validation dataset. In the segmentation phase, our segmented results achieved a dice score of 0.89845 in the whole tumor, 0.77734 in the tumor core, and 0.78957 in the enhancing tumor.…”

Section: B Dataset and Implementation Detailsmentioning

confidence: 99%

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Prediction of Survival of Glioblastoma Patients Using Local Spatial Relationships and Global Structure Awareness in FLAIR MRI Brain Images

et al. 2023

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This article introduces a framework for predicting the survival of brain tumor patients by analyzing magnetic resonance images. The prediction of brain tumor survival is challenging due to the limited size of available datasets. To overcome the issue of overfitting, we propose a self-supervised learning method that involves identifying image patches from the same or different images. By recognizing intra-and inter-image differences, the network can learn the relationships between local spatial windows in the same image and across different images. In addition to analyzing local information, we also incorporate a global structure awareness network to capture global information from the entire image. Our proposed method shows a strong correlation between local spatial relationships and survivor class prediction in FLAIR MRI brain images. We evaluate our method using the BraTS 2020 validation dataset and observe that our method outperforms others in accuracy and SpearmanR correlation metrics.

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Section: Related Workmentioning

confidence: 99%

Section: B Dataset and Implementation Detailsmentioning

confidence: 99%

Prediction of Survival of Glioblastoma Patients Using Local Spatial Relationships and Global Structure Awareness in FLAIR MRI Brain Images

et al. 2023

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“… Marti Asenjo and Martinez-Larraz Solís (2020) used an ensemble of four U-Net networks (three 2D U-Nets and one 3D U-Net) for the segmentation of brain tumors into three non-overlapping regions. The obtained multi-regional segmentation maps were used to extract a diverse set of radiomic features including first-order, shape, texture, and spatial features.…”

Section: Introductionmentioning

confidence: 99%

Overall Survival Prediction of Glioma Patients With Multiregional Radiomics

et al. 2022

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Radiomics-guided prediction of overall survival (OS) in brain gliomas is seen as a significant problem in Neuro-oncology. The ultimate goal is to develop a robust MRI-based approach (i.e., a radiomics model) that can accurately classify a novel subject as a short-term survivor, a medium-term survivor, or a long-term survivor. The BraTS 2020 challenge provides radiological imaging and clinical data (178 subjects) to develop and validate radiomics-based methods for OS classification in brain gliomas. In this study, we empirically evaluated the efficacy of four multiregional radiomic models, for OS classification, and quantified the robustness of predictions to variations in automatic segmentation of brain tumor volume. More specifically, we evaluated four radiomic models, namely, the Whole Tumor (WT) radiomics model, the 3-subregions radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model. The 3-subregions radiomics model is based on a physiological segmentation of whole tumor volume (WT) into three non-overlapping subregions. The 6-subregions and 21-subregions radiomic models are based on an anatomical segmentation of the brain tumor into 6 and 21 anatomical regions, respectively. Moreover, we employed six segmentation schemes – five CNNs and one STAPLE-fusion method – to quantify the robustness of radiomic models. Our experiments revealed that the 3-subregions radiomics model had the best predictive performance (mean AUC = 0.73) but poor robustness (RSD = 1.99) and the 6-subregions and 21-subregions radiomics models were more robust (RSD 1.39) with lower predictive performance (mean AUC 0.71). The poor robustness of the 3-subregions radiomics model was associated with highly variable and inferior segmentation of tumor core and active tumor subregions as quantified by the Hausdorff distance metric (4.4−6.5mm) across six segmentation schemes. Failure analysis revealed that the WT radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model failed for the same subjects which is attributed to the common requirement of accurate segmentation of the WT volume. Moreover, short-term survivors were largely misclassified by the radiomic models and had large segmentation errors (average Hausdorff distance of 7.09mm). Lastly, we concluded that while STAPLE-fusion can reduce segmentation errors, it is not a solution to learning accurate and robust radiomic models.

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Deriving and interpreting robust features for survival prediction of brain tumor patients

Rajput,

Kapdi,

Raval

et al. 2024

Int J Imaging Syst Tech

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Accurate prediction of survival days (SD) is vital for planning treatments in glioma patients, as type‐IV tumors typically have a poor prognosis and meager survival rates. SD prediction is challenging and heavily dependent on the extracted feature sets. Additionally, comprehending the behavior of complex machine learning models is a vital yet challenging aspect, particularly to integrate such models into the medical domain responsibly. Therefore, this study develops a robust feature set and an ensemble‐based regressor model to predict patients' SD accurately. We aim to understand how these features behave and contribute to predicting SD. To accomplish this, we employed post‐hoc interpretable techniques, precisely Shapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), and Accumulated Local Effects (ALE) plots. Furthermore, we introduced an investigation to establish a direct connection between radiomic features and their biological significance to enhance the interpretability of radiomic features. The best SD scores on the BraTS2020 training set are 0.504 for accuracy, 59927.38 mean squared error (MSE), 20101.86 median squared error (medianSE), and 0.725 Spearman ranking coefficient (SRC). The validation set's accuracy is 0.586, MSE is 76529.43, medianSE is 41402.78, and SRC is 0.52. The proposed predictor model exhibited superior performance compared with leading contemporary approaches across multiple performance metrics.

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MRI Brain Tumor Segmentation Using a 2D-3D U-Net Ensemble

Cited by 6 publications

References 16 publications

Prediction of Survival of Glioblastoma Patients Using Local Spatial Relationships and Global Structure Awareness in FLAIR MRI Brain Images

Prediction of Survival of Glioblastoma Patients Using Local Spatial Relationships and Global Structure Awareness in FLAIR MRI Brain Images

Overall Survival Prediction of Glioma Patients With Multiregional Radiomics

Deriving and interpreting robust features for survival prediction of brain tumor patients

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