D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation

14 Aug 2019Yongjin ZhouWeijian HuangPei DongYong XiaShanshan Wang

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... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Lesion Segmentation Anatomical Tracings of Lesions After Stroke (ATLAS) D-UNet Dice 0.5349 # 1
Lesion Segmentation Anatomical Tracings of Lesions After Stroke (ATLAS) D-UNet Precision 0.6331 # 1
Lesion Segmentation Anatomical Tracings of Lesions After Stroke (ATLAS) D-UNet Recall 0.5243 # 1