  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.
