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

arXiv: https://arxiv.org/abs/2105.07962 · HTML: https://mlsanity.com/paper/2105.07962/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [Receptive Field Block Net for Accurate and Fast Object Detection](https://mlsanity.com/paper/1711.07767/) (2017)
- [ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation](https://mlsanity.com/paper/1907.10936/) (2019)
- [X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies](https://mlsanity.com/paper/1907.07000/) (2019)
- [Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review](https://mlsanity.com/paper/1712.03747/) (2017)
- [Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities](https://mlsanity.com/paper/1810.07003/) (2018)
- [Deep Neural Networks for Anatomical Brain Segmentation](https://mlsanity.com/paper/1502.02445/) (2015)
- [Multi-scale Attention U-Net (MsAUNet): A Modified U-Net Architecture for Scene Segmentation](https://mlsanity.com/paper/2009.06911/) (2020)
- [Optimizing Speech Emotion Recognition using Manta-Ray Based Feature Selection](https://mlsanity.com/paper/2009.08909/) (2020)

## Built on by (most cited first, 4 of 4)

- [Addressing Class Imbalance in Semi-supervised Image Segmentation: A Study on Cardiac MRI](https://mlsanity.com/paper/2209.00123/) (2022)
- [Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection](https://mlsanity.com/paper/2106.04919/) (2021)
- [SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization](https://mlsanity.com/paper/2205.04329/) (2022)
- [FAN-Net: Fourier-Based Adaptive Normalization For Cross-Domain Stroke Lesion Segmentation](https://mlsanity.com/paper/2304.11557/) (2023)

## Abstract (arXiv)

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.