Extending nn-UNet for brain tumor segmentation (2112.04653v1)

Abstract: Brain tumor segmentation is essential for the diagnosis and prognosis of patients with gliomas. The brain tumor segmentation challenge has continued to provide a great source of data to develop automatic algorithms to perform the task. This paper describes our contribution to the 2021 competition. We developed our methods based on nn-UNet, the winning entry of last year competition. We experimented with several modifications, including using a larger network, replacing batch normalization with group normalization, and utilizing axial attention in the decoder. Internal 5-fold cross validation as well as online evaluation from the organizers showed the effectiveness of our approach, with minor improvement in quantitative metrics when compared to the baseline. The proposed models won first place in the final ranking on unseen test data. The codes, pretrained weights, and docker image for the winning submission are publicly available at https://github.com/rixez/Brats21_KAIST_MRI_Lab

• The paper introduces modifications to nn-UNet such as asymmetric encoder expansion to enhance brain tumor segmentation accuracy.
• It replaces batch normalization with group normalization to better manage low batch sizes in 3D mpMRI scans.
• Axial attention is integrated into the decoder to improve long-range dependency modeling, yielding consistent gains in Dice and Hausdorff metrics.

Extending nn-UNet for Brain Tumor Segmentation: A Comprehensive Analysis

The paper "Extending nn-UNet for brain tumor segmentation" introduces a series of modifications to the established nn-UNet framework, applied in the context of the BraTS 2021 competition. This investigation is concentrated on enhancing the segmentation of brain tumors from multi-parametric MRI (mpMRI) data, pivotal in aiding clinical diagnosis and treatment of gliomas. The paper's focus lies in evaluating the impact of architectural adjustments to the nn-UNet on the performance of automatic brain tumor segmentation.

Modifications to the nn-UNet Framework

1. Network Architecture Enhancements: The authors propose an asymmetrical enlargement of the encoder segment of the nn-UNet. This involves doubling the filter numbers in the encoder while retaining the decoder's filter numbers. The network's maximum filters were extended up to 512, reflecting an attempt to leverage the increased data availability from the 2021 BraTS dataset, which comprises substantially more samples than previous years.
2. Normalization Strategy: Another innovation is the substitution of batch normalization with group normalization. This change addresses the memory constraints associated with running 3D convolutional neural networks, which typically necessitate smaller batch sizes. Group normalization has demonstrated superior efficacy in low batch size regimes.
3. Incorporation of Axial Attention: The paper incorporates axial attention in the network's decoder. Axial attention, an advancement on self-attention mechanisms tailored for high dimensional data, is intended to optimize the computational efficiency and capture of long-range dependencies within the 3D MRI volumes.

Experimental Design and Results

The experiments were conducted using a dataset of 2000 mpMRI scans from the BraTS 2021 competition, with segmentation labels provided for 1251 cases. The methods underwent rigorous internal 5-fold cross-validation alongside evaluation from the competition's organizers. The results manifested that the modifications yielded a minor yet consistent improvement in Dice score and Hausdorff distance metrics compared to the baseline nn-UNet.

• Quantitative Findings: Incremental improvements were noted in tumor core (TC) and whole tumor (WT) segmentation across models incorporating the outlined modifications. The use of group normalization indicated a slight adverse effect on performance metrics, an observation that underscores the intricacies of balancing computational changes with model efficacy.
• Qualitative Observations: Analyzing representative segmentation outputs revealed that prediction accuracy was contingent on input data quality, with blurring and artifacts significantly impairing performance. This insight highlights the necessity for preprocessing robustness and potentially more sophisticated data augmentation techniques.

Implications and Future Directions

The extensions proposed in this paper affirm the adaptability and robustness of nn-UNet, particularly its capacity to integrate new components and scale with dataset enlargements. The marginal improvements argue for cautious optimism; they underscore the potential returns from tuning network architectures in the face of burgeoning medical imaging datasets.

For future investigations, there exists potential for further exploration into dynamic normalization techniques and more advanced attention mechanisms. Moreover, segmentation pipelines might benefit from advanced data cleansing procedures to mitigate the detrimental effects of low-quality input scans.

In summary, this paper presents meaningful, albeit modest, advancements in the field of brain tumor segmentation. The approaches delineated herein provide fertile ground for continued exploration, particularly in the field where the confluence of computer vision and deep learning techniques continues to hold promising potential for clinical application.