Automated brain extraction of multi-sequence MRI using artificial neural networks (1901.11341v2)

Abstract: Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aims to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.11 points for the DICE coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility our HD-BET prediction algorithm is made freely available (http://www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.

• The paper presents HD-BET, a novel neural network that improves brain extraction accuracy with median Dice scores between 96.1% and 97.6%.
• It employs a U-Net inspired architecture trained on over 6,586 MRI sequences and validated on 3,419 diverse cases, including pathological variations.
• Comparative results show HD-BET outperforms six established methods in both segmentation precision and processing speed, enhancing clinical workflows.

An Analysis of Automated Brain Extraction Using Neural Networks

The paper "Automated Brain Extraction of Multi-sequence MRI Using Artificial Neural Networks" presents HD-BET, an advanced algorithm leveraging artificial neural networks for brain extraction in MRI data. Its notable contribution lies in outperforming existing methods in diverse and heterogeneous datasets, which contain pathological variances and have been challenging for previous techniques.

Brain extraction is an essential preprocessing step in neuroimaging that affects the accuracy of subsequent analyses. The paper underscores the limitations of traditional brain extraction algorithms that typically excel on MRI images from healthy individuals but struggle with images containing brain tumors or other pathological conditions. HD-BET is designed to address these challenges by maintaining robust performance across various MRI sequences and parameters.

Methodology and Evaluation

The development of HD-BET utilizes data from the EORTC-26101 trial and other public datasets, ensuring rigorous validation. The authors employ an artificial neural network inspired by the U-Net architecture, which has already demonstrated efficacy in related medical imaging tasks. Incorporating a variety of datasets, HD-BET was trained on 6,586 MRI sequences and tested on another independent set of 3,419 sequences, demonstrating its capacity to generalize across different institutional data sources.

The algorithm is evaluated using two key metrics—Dice coefficient and Hausdorff distance. The reported median Dice coefficients for HD-BET across various MRI sequences range from 96.1 to 97.6, reflecting excellent spatial similarity with the ground truth. Similarly, the Hausdorff distance, a measure of boundary accuracy, remains low, further validating HD-BET's efficiency in precise segmentation.

Comparative Performance

The paper contrasts HD-BET’s performance with six other brain extraction algorithms, namely BET, 3dSkullStrip, BSE, ROBEX, BEaST, and MONSTR. Notably, the HD-BET shows consistent superiority across all test datasets, with improvements ranging from +1.16 to +2.50 in Dice coefficient and -0.66 to -2.51 mm in Hausdorff distance.

The algorithm extends its advantages by performing robustly on non-T1-weighted sequences such as T2, FLAIR, and cT1-w, areas where many traditional algorithms are limited. MONSTR, the closest competitor specially designed for pathological cases, lags in performance and computational efficiency, requiring significantly more processing time compared to HD-BET.

Implications and Future Directions

The research implications of this paper address critical challenges in automated neuroimaging. By overcoming the limitations of existing algorithms in handling pathological variations and different MRI sequence types, HD-BET provides a reliable tool for high-throughput image analysis necessary for both clinical and research settings.

Practically speaking, HD-BET’s robust and fast performance is imperative in clinical environments where processing speed and accuracy can directly influence patient outcomes. The freely available implementation of HD-BET potentially democratizes access to cutting-edge brain extraction technology, serving as an integral component of neuroimaging workflows.

Theoretically, the development of HD-BET highlights the advancements in machine learning applied to medical imaging, showcasing how neural networks can transcend conventional methods by learning complex patterns associated with pathological variances.

Future work should focus on expanding HD-BET’s applicability across a wider spectrum of neurological disorders beyond the scope of brain tumors, evaluating its efficacy in pathologies such as neurodegenerative diseases or traumatic brain injuries. The alignment of accurate brain extraction techniques with evolving AI capabilities is anticipated to catalyze further breakthroughs in neuroimaging analysis and interpretation.