Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features (1812.01049v1)

Abstract: Accurate segmentation of different sub-regions of gliomas including peritumoral edema, necrotic core, enhancing and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape, segmentation of the sub-regions is very challenging. Recent development using deep learning models has proved its effectiveness in the past several brain segmentation challenges as well as other semantic and medical image segmentation problems. Most models in brain tumor segmentation use a 2D/3D patch to predict the class label for the center voxel and variant patch sizes and scales are used to improve the model performance. However, it has low computation efficiency and also has limited receptive field. U-Net is a widely used network structure for end-to-end segmentation and can be used on the entire image or extracted patches to provide classification labels over the entire input voxels so that it is more efficient and expect to yield better performance with larger input size. Furthermore, instead of picking the best network structure, an ensemble of multiple models, trained on different dataset or different hyper-parameters, can generally improve the segmentation performance. In this study we propose to use an ensemble of 3D U-Nets with different hyper-parameters for brain tumor segmentation. Preliminary results showed effectiveness of this model. In addition, we developed a linear model for survival prediction using extracted imaging and non-imaging features, which, despite the simplicity, can effectively reduce overfitting and regression errors.

• The paper presents an ensemble of 3D U-Nets that enhances segmentation accuracy by averaging outputs and leveraging probabilistic patch extraction.
• It introduces a non-uniform patch extraction strategy to address class imbalance and memory constraints in processing high-dimensional MRI scans.
• The study demonstrates a linear regression approach that effectively predicts patient survival using radiomic and clinical features, excelling on testing data.

Brain Tumor Segmentation and Survival Prediction Using Advanced Machine Learning Techniques

The paper "Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features" presents a methodology that enhances the accuracy and efficiency of brain tumor segmentation and survival prediction. The authors propose an innovative approach using deep learning, specifically an ensemble of 3D U-Net architectures, to address the complexity and heterogeneity of gliomas extracted from multimodal MRI scans.

Methodology Overview

The paper tackles two primary tasks: segmenting distinct glioma sub-regions and predicting patient survival post-surgery. The proposed model employs an ensemble of 3D U-Nets, which have demonstrated efficacy in capturing complex features due to their comprehensive receptive fields. The authors circumvent memory constraints typically associated with high-dimensional inputs by utilizing non-uniform patch extraction, thus enhancing computational efficiency and alleviating class imbalance issues.

Key procedures in this methodology include:

• Pre-processing: Leveraging bias correction and denoising techniques to ameliorate image quality.
• Patch Extraction: Using a probabilistic approach to selectively extract informative patches, prioritizing tumor voxels to address class imbalance.
• Network Configuration: Training six distinct models with varying hyperparameters, including patch size and loss weighting, to enhance model diversity and robustness.
• Ensemble Approach: Averaging the probabilistic outputs of multiple U-Nets for improved segmentation performance over individual models.

In the survival prediction task, a linear regression model is constructed using both radiomic features and clinical data, aiming to minimize overfitting potential.

Results and Implications

The ensemble of 3D U-Nets outperformed each constituent model when evaluated on dice scores and Hausdorff distances across different tumor sub-regions. Notably, the ensemble achieved a mean dice score of 0.7917 for the enhancing tumor and strong performance in other categories, validating the efficacy of model averaging strategies in medical image segmentation.

For survival prediction, while the model's performance on validation data was modest compared to competitors, it excelled on the testing dataset, securing the top position. This suggests that the linear model, despite its simplicity, benefits from reduced overfitting risks, an evident advantage in small datasets prevalent in medical imaging challenges.

Theoretical and Practical Implications

The paper underscores the potential of ensemble learning frameworks in medical imaging, providing a groundwork for further exploration into complex model architectures. The ability of 3D U-Nets to leverage extensive contextual information while maintaining precise localization paves the way for higher-dimensional implementations with enhanced interpretability.

Practically, these insights can significantly impact clinical workflows by facilitating more precise and expedited tumor delineation, thus supporting more informed decision-making in treatment planning. The robustness of the survival prediction model, even in the absence of extensive histological or genetic data, emphasizes the viability of imaging-based prognostic models.

Future Directions

Future research may focus on integrating additional clinical features, such as genetic markers, to further hone survival prediction models. The expansion of computational resources could also allow for larger ensembles, potentially improving segmentation performance through increased model diversity.

Enhancements in model interpretability and deployment efficiency, particularly in edge cases deviating from the training dataset, are also critical areas for development. Continued interdisciplinary collaboration is essential to translate these technological advancements into tangible patient care improvements.

In conclusion, this paper contributes a significant advancement in the application of deep learning for brain tumor analysis, reflecting the transformational potential of AI in medical imaging. The employment of an ensemble approach not only enhances segmentation accuracy but also offers a robust mechanism for overcoming the inherent challenges posed by imaging heterogeneity.