Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities (1610.04834v2)

Abstract: The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with hand-crafted features as well as CNNs that do not integrate location information. On a test set of 46 scans, the best configuration of our networks obtained a Dice score of 0.791, compared to 0.797 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value=0.17).

• The paper integrates anatomical location features into CNN architectures to improve white matter hyperintensity segmentation.
• It employs multi-scale fusion strategies and explicit spatial cues, achieving a Dice score of 0.791 that closely mirrors human performance.
• The study highlights the potential of location-sensitive neural networks to advance automated diagnostics in complex medical imaging applications.

Overview of Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities

The paper "Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities," authored by Ghafoorian et al., presents an in-depth paper on the integration of anatomical location features into convolutional neural networks (CNNs) for the task of white matter hyperintensities (WMHs) segmentation. The researchers aim to address the limitations of traditional CNNs, which lack inherent mechanisms for incorporating anatomical context, thus hindering their efficacy in certain medical imaging tasks.

Methodology

The authors propose several architectural enhancements to standard CNNs to incorporate anatomical location information. They explore architectures that use either multi-scale patches or explicit spatial location features during training. The proposed architectures include single-scale models and multi-scale fusion networks, with variations on early and late fusion strategies. The use of each scale was examined with options for independent weight allocation or weight sharing. Additionally, eight spatial location features were integrated into network architectures to ascertain the most effective incorporation point within the CNN.

The paper evaluates these models on a large dataset of over 500 brain MR images. The performance is compared against a conventional segmentation method equipped with hand-crafted features and human observers, utilizing Dice scores as the primary metric.

Results

Quantitative analysis reveals that CNNs incorporating anatomical location information substantially outperform traditional methods. The most effective network configuration, a multi-scale late fusion with weight sharing and explicit location features (MSWS+Loc), achieved a Dice score of 0.791, closely matching an independent human observer's score of 0.797. The similarity in performance between the MSWS+Loc model and human observers (p-value=0.17) implies that the model effectively segments WMHs at a level comparable to human experts.

Implications and Future Work

The integration of location-sensitive features in CNNs represents a significant advancement in the segmentation of WMHs, creating pathways for potential applications across other types of medical imaging requiring spatial contextual awareness. These findings encourage further exploration of architectures exploiting spatial context in CNNs, potentially broadening the scope of automating complex medical diagnostic tasks with high precision.

The work provokes interesting future research directions, such as the application of 3D CNNs in datasets with isotropic resolution, or fully convolutional networks for enhanced computational efficiency. Further refinement in dealing with variability in image acquisition protocols and integrating multimodal imaging data could further improve network robustness and applicability in clinical settings.

In conclusion, this research contributes a sophisticated approach to automated WMH segmentation, demonstrating the critical role of anatomical location in enhancing CNN-based medical image analysis systems. As the field progresses, adopting intelligent systems that effectively mirror human interpretative capabilities becomes increasingly feasible, potentially augmenting medical diagnostics and patient care outcomes.