  Assessing the location and extent of lesions caused by chronic stroke is
critical for medical diagnosis, surgical planning, and prognosis. In recent
years, with the rapid development of 2D and 3D convolutional neural networks
(CNN), the encoder-decoder structure has shown great potential in the field of
medical image segmentation. However, the 2D CNN ignores the 3D information of
medical images, while the 3D CNN suffers from high computational resource
demands. This paper proposes a new architecture called dimension-fusion-UNet
(D-UNet), which combines 2D and 3D convolution innovatively in the encoding
stage. The proposed architecture achieves a better segmentation performance
than 2D networks, while requiring significantly less computation time in
comparison to 3D networks. Furthermore, to alleviate the data imbalance issue
between positive and negative samples for the network training, we propose a
new loss function called Enhance Mixing Loss (EML). This function adds a
weighted focal coefficient and combines two traditional loss functions. The
proposed method has been tested on the ATLAS dataset and compared to three
state-of-the-art methods. The results demonstrate that the proposed method
achieves the best quality performance in terms of DSC = 0.5349+0.2763 and
precision = 0.6331+0.295).
