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An Exhaustive Analytical Study of U-Net Architecture on Two Diverse Biomedical Imaging Datasets of Electron Microscopy Drosophila ssTEM and Brain MRI BraTS-2021 for Segmentation


Biomedical image segmentation is a mechanism that distinguishes the boundaries of various lesion regions within the image, be it 2D or 3D. In the current scenario, various soft and hard computing approaches have been used for medical image segmentation purposes. This article analyzes deep learning-based U-Net architecture for efficient segmentation by possible variations of its different important hyper-parameters that affect the U-Net performance. This article also investigates U-Net architecture’s performance to generate the segmented output over two diverse biomedical imaging datasets, such as brain MRI scans and serial section Transmission Electron Microscopy (ssTEM) dataset of the drosophila first instar larva VNC, hence making 48 possible combinations of execution of the U-Net model. The performance of the segmentation process has been evaluated using metrics like dice similarity coefficient and accuracy, while cross-entropy is considered the loss function. In doing so, an insight into the overall performance of U-Net on biomedical image segmentation problems has been obtained. The best results for accuracy and dice coefficient values out of all possible combinations made in this study come as 0.9881 and 0.9934, respectively. The main motive of this work is to provide an exhaustive and efficient analysis of the U-Net architecture for biomedical image segmentation and subsequent analysis.

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Jena, B., Nayak, G.K., Paul, S. et al. An Exhaustive Analytical Study of U-Net Architecture on Two Diverse Biomedical Imaging Datasets of Electron Microscopy Drosophila ssTEM and Brain MRI BraTS-2021 for Segmentation. SN COMPUT. SCI. 3, 418 (2022).

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  • Deep learning
  • Medical image segmentation
  • Brain MRI (magnetic resonance imaging)
  • Electron microscopy dataset
  • U-Net
  • Ventral nerve cord (VNC)