DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation

TL;DR

DFENet is a new brain MRI segmentation network that fuses 2D and 3D features, using edge guidance and a special decoder to improve accuracy and efficiency in stroke lesion detection.

Contribution

The paper introduces DFENet, a novel fusion-based network with a parallel partial decoder and edge supervision, advancing brain MRI segmentation accuracy and computational efficiency.

Findings

Achieved higher DSC, IoU, Precision, and Recall than existing methods.

Outperformed state-of-the-art models on the ATLAS dataset.

Demonstrated robustness and reliability for biomedical stroke analysis.

Abstract

The rapid increment of morbidity of brain stroke in the last few years have been a driving force towards fast and accurate segmentation of stroke lesions from brain MRI images. With the recent development of deep-learning, computer-aided and segmentation methods of ischemic stroke lesions have been useful for clinicians in early diagnosis and treatment planning. However, most of these methods suffer from inaccurate and unreliable segmentation results because of their inability to capture sufficient contextual features from the MRI volumes. To meet these requirements, 3D convolutional neural networks have been proposed, which, however, suffer from huge computational requirements. To mitigate these problems, we propose a novel Dimension Fusion Edge-guided network (DFENet) that can meet both of these requirements by fusing the features of 2D and 3D CNNs. Unlike other methods, our proposed network uses a parallel partial decoder (PPD) module for aggregating and upsampling selected features, rich in important contextual information. Additionally, we use an edge-guidance and enhanced mixing loss for constantly supervising and improvising the learning process of the network. The proposed method is evaluated on publicly available Anatomical Tracings of Lesions After Stroke (ATLAS) dataset, resulting in mean DSC, IoU, Precision and Recall values of 0.5457, 0.4015, 0.6371, and 0.4969 respectively. The results, when compared to other state-of-the-art methods, outperforms them by a significant margin. Therefore, the proposed model is robust, accurate, superior to the existing methods, and can be relied upon for biomedical applications.