Hybridization of Attention UNet with Repeated Atrous Spatial Pyramid Pooling for Improved Brain Tumour Segmentation (2501.13129v1)

Abstract: Brain tumors are highly heterogeneous in terms of their spatial and scaling characteristics, making tumor segmentation in medical images a difficult task that might result in wrong diagnosis and therapy. Automation of a task like tumor segmentation is expected to enhance objectivity, repeatability and at the same time reducing turn around time. Conventional convolutional neural networks (CNNs) exhibit sub-par performance as a result of their inability to accurately represent the range of tumor sizes and forms. Developing on that, UNets have been a commonly used solution for semantic segmentation, and it uses a downsampling-upsampling approach to segment tumors. This paper proposes a novel architecture that integrates Attention-UNet with repeated Atrous Spatial Pyramid Pooling (ASPP). ASPP effectively captures multi-scale contextual information through parallel atrous convolutions with varying dilation rates. This allows for efficient expansion of the receptive field while maintaining fine details. The attention provides the necessary context by incorporating local characteristics with their corresponding global dependencies. This integration significantly enhances semantic segmentation performance. Our approach demonstrates significant improvements over UNet, Attention UNet and Attention UNet with Spatial Pyramid Pooling allowing to set a new benchmark for tumor segmentation tasks.

• The paper proposes a novel deep learning model combining Attention UNet and Atrous Spatial Pyramid Pooling (ASPP) to improve brain tumor segmentation accuracy and handle diverse tumor shapes and sizes.
• Quantitative evaluation using the BraTS 2023 dataset shows the hybrid model outperforms standard methods, achieving a Dice Similarity Coefficient up to 79.75 and mIoU of 45.83.
• The proposed architecture demonstrates enhanced efficiency and potential for real-time clinical application by integrating attention mechanisms with multi-scale feature capture.

The paper "Hybridization of attention unet with repeated atrous spatial pyramid pooling for improved brain tumor segmentation" introduces a novel deep learning architecture designed to tackle the complexities of brain tumor segmentation from MRI images. The proposed method converges the strengths of Attention UNet with the Atrous Spatial Pyramid Pooling (ASPP) to enhance the segmentation performance. This integration is aimed at addressing the challenges posed by the heterogeneity in tumor sizes and forms in the MRI data.

Methodology

Architecture Overview

The architecture is predicated on the UNet model, which is well-regarded for semantic segmentation tasks, particularly within the medical imaging domain. The modifications presented in this research involve:

1. Attention Mechanisms: Built upon the base UNet, the attention mechanism selectively enhances relevant features while downplaying irrelevant information in each layer. The Attention UNet framework is used to incorporate gates that modulate feature responses, significantly reducing false-positive rates in diverse lesion appearances.
2. Atrous Spatial Pyramid Pooling (ASPP): The ASPP component enables the model to capture multi-scale contextual information crucial for effective segmentation of structures with diverse spatial extents and precise boundary definitions. The ASPP introduces atrous convolutions with variable dilation rates, thus expanding the receptive field without a concomitant loss of resolution.
3. End-to-End Integration: The proposed network integrates multiple ASPP blocks and attention gates within the UNet structure, thus achieving enhanced extraction and discriminative segmentation by retaining spatial details while considering global contextual information.

Quantitative Assessment

The authors provide a detailed quantitative evaluation of their model using the BraTS 2023 dataset, showcasing its superiority in segmenting brain tumors. The model's performance metrics are evaluated using DSC (Dice Similarity Coefficient), mIoU (mean Intersection over Union), and Acc (Accuracy) across various MRI modalities such as T1C, T2 FLAIR, and T2W. The results show a notable improvement compared to standard UNet and Attention UNet models, with the hybrid model achieving a DSC as high as 79.75 and mIoU of 45.83 on certain modalities.

Experimental Results

The proposed Attention UNet with ASPP not only outperforms existing models in terms of segmentation accuracy but also demonstrates efficiency in computational time. The model showcased enhanced training run times and inference times in comparison with baseline architectures, highlighting its applicability for real-time clinical use.

Conclusions

The paper concludes that the integration of attention mechanisms with ASPP blocks significantly bolsters the representational capabilities of CNN-based architectures, specifically in the domain of brain tumor segmentation. The ability of the proposed model to handle diverse tumor morphologies and spatial scales illustrates its potential utility in clinical settings, offering granular insights needed for improved diagnostic and treatment planning processes. Future research directions could entail optimizing the model further for different medical imaging tasks, thereby generalizing its applicability across a broader spectrum of clinical conditions.