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

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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.

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The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Electrical Engineering, Jadavpur University, 188, Raja S.C. Mullick Road, Jadavpur, Kolkata, West Bengal, 700032, India

    Hritam Basak & Rukhshanda Hussain

  2. Department of Computer Science Engineering, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, Tamil Nadu, 603203, India

    Ajay Rana

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  1. Hritam Basak
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Correspondence to Hritam Basak.

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Hritam Basak declares that he has no conflict of interest. Rukhshanda Hussain declares that she has no conflict of interest. Ajay Rana declares that he has no conflict of interest.

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This article is part of the topical collection “Progresses in Image Processing” guest edited by P. Nagabhushan, Peter Peer, Partha Pratim Roy and Satish Kumar Singh.

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Basak, H., Hussain, R. & Rana, A. DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation. SN COMPUT. SCI. 2, 435 (2021). https://doi.org/10.1007/s42979-021-00835-x

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