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YSegNet: a novel deep learning network for kidney segmentation in 2D ultrasound images

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Abstract

The prior step in most of the kidney related image analysis is precise and authentic kidney segmentation. Although many semi-automated kidney segmentation techniques for 2D ultrasound images have been proposed, only a minimal number of automated techniques were explored. Low contrast and variability in the shape of kidney poses a challenge for the same. In this paper, an YSegNet based on an encoder–decoder network combined with a boundary extraction network is presented. The proposed model and its variants have been experimented using a VGG-16 encoder from scratch and a pre-trained VGG-16 encoder. A dataset of 700 images were considered for experimentation, which is further augmented to increase the size of the dataset for better precision. The proposed network is compared with basic U-Net and similar competing deep learning networks. The segmented map is promising with an accuracy of 97.26%, DICE score of 0.97, specificity of 0.97 and sensitivity of 0.98, thereby confirming that the presented deep learning segmentation network can used in automated 2D ultrasound kidney image analysis.

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The data supporting the findings of this work are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of ECE, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, Tamil Nadu, 641 114, India

    Deepthy Mary Alex & D. Abraham Chandy

  2. Department of Mathematics, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, Tamil Nadu, 641114, India

    A. Hepzibah Christinal

  3. Department of Radiology, Sri Guru Ram Das Institute of Medical Sciences and Research, Sri Amritsar, Punjab, 143501, India

    Arvinder Singh

  4. Radiology Division, Kovai Diagnostic Centre, Coimbatore, Tamil Nadu, 641012, India

    M. Pushkaran

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Alex, D.M., Abraham Chandy, D., Hepzibah Christinal, A. et al. YSegNet: a novel deep learning network for kidney segmentation in 2D ultrasound images. Neural Comput & Applic 34, 22405–22416 (2022). https://doi.org/10.1007/s00521-022-07624-4

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