Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation (1810.07884v2)

Abstract: Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.

Automatic Brain Tumor Segmentation using CNNs with Test-Time Augmentation

The paper addresses the challenge of brain tumor segmentation in medical imaging using deep learning methods, particularly convolutional neural networks (CNNs), with an emphasis on enhancing model robustness through test-time augmentation (TTA). The focus is on overcoming the inherent variability in the appearance of gliomas and the limited amount of training data typically available for medical imaging applications.

Background and Methodology

Gliomas, particularly high-grade gliomas, pose significant challenges for diagnosis and treatment planning due to their heterogeneous nature and diffuse boundaries when observed through magnetic resonance imaging (MRI). The task of segmentation is further complicated by the need for accurate delineation across different MRI modalities (T1, T1ce, T2, FLAIR), each emphasizing various aspects of the tumor.

The authors leverage CNNs, which have become the state-of-the-art in medical image segmentation due to their capability to automatically learn hierarchical features from raw pixel data. They highlight the use of data augmentation, a crucial step to increase the effective size of the training dataset, thus allowing for improved generalization of the model. Traditional approaches primarily apply augmentations during training, but this paper investigates the augmentation of data at test time to improve the robustness and accuracy of predictions.

The paper extends the work of previous studies by incorporating TTA not only to improve segmentation performance but also to enable uncertainty estimation of the segmentation outputs. The methodology involves performing multiple transformations, such as 3D rotation, flipping, scaling, and adding random noise to the test images, and subsequently aggregating the individual predictions for a more robust final segmentation. This is achieved using a Monte Carlo simulation approach to sample augmented test images and derive a consensus prediction via majority voting.

Implementation and Results

Three network architectures were explored in this paper: 3D U-Net, a multi-class variant of WNet, and a cascaded network structure. These models were trained and evaluated using the BraTS 2018 dataset, featuring a substantial number of patient scans for a comprehensive assessment of tumor segmentation performance. Through quantitative evaluation metrics, the authors report improvements in Dice scores and Hausdorff distances with the application of TTA, illustrating its beneficial impact on segmentation accuracy across different network architectures.

In addition to segmentation performance metrics, the paper addresses the challenge of estimating uncertainties in the prediction maps, a crucial aspect for enhancing clinical trust in AI systems. The use of TTA allows for the estimation of aleatoric uncertainty by examining the variability in predictions across augmented test samples, represented through measures such as entropy.

Implications and Future Work

The findings underscore the potential of TTA as a straightforward yet effective technique for improving segmentation performance in medical imaging contexts where test data is susceptible to transformations and variations. The enhancement of CNN-based segmentation methods through TTA has implications for clinical workflows, potentially improving the reliability and usability of AI-assisted diagnostic tools.

Future explorations are suggested in areas such as the integration of epistemic uncertainty using model ensembles or dropout strategies combined with TTA, which could provide a more holistic uncertainty estimation framework. These advancements will be pivotal in driving further AI adoption in clinical settings by prioritizing model robustness and interpretability.

In conclusion, this work contributes to the ongoing efforts to refine AI techniques for medical image analysis, offering a practical approach to address challenges in brain tumor segmentation, and sets a foundation for continued exploration into augmenting CNN-based methodologies with TTA for multifaceted improvements.