Advancing Brain Tumor Detection with Deep Learning and Medical Imaging Datasets
Brain tumors are a devastating medical condition with significant public health impact. According to recent global statistics, there were an estimated 308,102 new cases of brain and central nervous system tumors and 251,329 deaths in 2020 alone [1]. Gliomas, which arise from glial cells in the brain, are among the most common and deadly. Glioblastoma multiforme (GBM), the most aggressive glioma subtype, has a median survival of only 15 months even with intensive treatment [2].
Early and accurate detection is critical to give brain tumor patients the best chance at survival. Medical imaging techniques like magnetic resonance imaging (MRI) and computed tomography (CT) allow non-invasive visualization of the brain. Interpreting these scans requires extensive training and experience. Brain tumors can be subtle and easy to miss – for example, low-grade gliomas may appear only as slight signal abnormalities.
Precisely segmenting brain tumors is also highly time-consuming. In one study, manual segmentation of a single patient‘s GBM tumor took an average of over 60 minutes [3]. Automatic tumor segmentation methods are thus highly desirable. However, this is a challenging task due to the heterogeneity of tumor appearance across patients and the potential presence of other pathologies like stroke lesions.
Deep learning has revolutionized medical image analysis in recent years, demonstrating remarkable performance on tasks like tumor detection, segmentation, and classification. Convolutional neural networks (CNNs) in particular have an inductive bias well-suited to learning from grid-like image data.
The availability of large public datasets has catalyzed progress in deep learning for brain tumor analysis. The Multimodal Brain Tumor Segmentation Challenge (BraTS), organized annually since 2012, has released a dataset of multi-institutional routine clinically-acquired pre-operative multimodal MRI scans of glioma patients [4]. The 2020 training data includes 369 GBM cases and 293 LGG cases, with each case including four MRI scans (T1, post-contrast T1, T2, and FLAIR) and manual segmentation of the GD-enhancing tumor, peritumoral edema, and necrotic and non-enhancing tumor core. This dataset has become a standard benchmark for brain tumor segmentation algorithms.
Preprocessing is a critical first step to prepare medical images for deep learning models. Brain extraction (or "skull stripping") is commonly performed to remove non-brain tissue from images using tools like FSL‘s BET [5], AFNI‘s 3dSkullStrip [6], or ANTs‘ antsBrainExtraction [7]. N4 bias field correction is used to remove low-frequency intensity non-uniformity artifacts [8]. Since MRI scans can have widely varying intensity distributions across patients and scanners, intensity normalization is important to standardize each modality to a consistent range.
Due to the volumetric nature of brain MRI data, 3D CNNs are commonly employed to learn features across all three spatial dimensions. However, 3D convolutions are computationally expensive in terms of memory and runtime. 2D CNNs trained on slice-wise images are more efficient and can still achieve good performance, though they sacrifice contextual information. Some studies have found better performance with pseudo-3D CNNs that use 2D convolutions in each orthogonal plane, or with 2.5D approaches that concatenate neighboring slices as input channels to provide some 3D context [9].
The U-Net architecture has been particularly successful for brain tumor segmentation [10]. Its encoder-decoder structure and skip connections preserve spatial information to produce a full-resolution segmentation map. The original U-Net has been extended with residual connections [11], dense connections [12], attention gates [13], and cascaded frameworks [14]. Ensembles of different U-Net variants have achieved top performance on the BraTS challenge, with Dice scores over 0.9 for whole tumor and tumor core segmentation [15].
However, supervised learning with paired images and annotations is limited by the expense of collecting expert segmentations. Self-supervised learning (SSL) is a promising approach to leverage large-scale unlabeled data. SSL methods learn useful representations by solving pretext tasks like context restoration [16], contrastive predictive coding [17], or rotation prediction [18]. SSL backbones pretrained on unlabeled data have matched the performance of supervised approaches while using an order of magnitude less labeled data [19].
Anomaly detection is another approach that aims to detect tumors in the absence of pixelwise annotations. Unsupervised methods like variational autoencoders [20] and GANs [21] can be trained on healthy brain scans to flag abnormal cases by reconstruction error or discriminator score, respectively. These models could be used to prioritize scans for radiologist review in screening contexts. However, they may suffer from high false positive rates due to difficulty capturing the full range of normal anatomical variation.
For AI assistance to be adopted in clinical practice, it must be integrated into radiologists‘ existing software tools and workflows. Explainable AI techniques should be employed to provide interpretable visualizations of the factors influencing a model‘s predictions [22]. This is important for building trust and identifying potential failure modes. Careful validation must be performed, not only on held-out subsets of public datasets like BraTS, but also on private institutional datasets to assess generalization to real-world data distributions. Prospective studies with radiologists are necessary to measure improvements in accuracy and efficiency from AI assistance.
Centralized datasets like BraTS have accelerated research progress, but federated learning [23] is an appealing framework to train generalizable models across multiple institutions without sharing sensitive patient data. In federated learning, each institution trains a local model on their own data, and a central server aggregates the model weights (not the data) to update a global model. This could enable large-scale multi-institutional collaborations to build robust models while preserving data privacy.
Remaining challenges in brain tumor detection with AI include improving performance on rare tumor subtypes, distinguishing treatment effects and other pathologies from tumor progression, and validating generalization to emerging MRI protocols and scanner hardware. As deep learning models become more powerful, it will be important to develop techniques to detect and mitigate potential biases, such as disparities in performance across patient demographics. Interpretability methods to visualize a model‘s decision process and sensitivity to different inputs will be key to building trust with clinicians and identifying potential failure modes.
In conclusion, AI-based brain tumor detection and segmentation has made significant strides thanks to the advent of deep learning and large public datasets. Continued advances in architectures, unsupervised learning, and federated learning hold great promise to make AI an indispensable tool for radiologists. Close collaboration between AI researchers and clinical stakeholders will be essential to translate these advances into meaningful patient impact. With further progress, we can hope for a future in which AI-assisted diagnosis drives earlier detection and better outcomes for patients afflicted with brain tumors.
