Deep Neural Networks for Medical Image Segmentation

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  • Priyanka Malhotra
    Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India
  • Sheifali Gupta
    Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India
  • Deepika Koundal
    Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
  • Atef Zaguia
    Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
  • Wegayehu Enbeyle
    Department of Statistics, Mizan-Tepi University, Tepi, Ethiopia
  • Chinmay Chakraborty
    editor

説明

<jats:p>Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of the organs, diseases, or abnormalities in medical images has become demanding. The segmentation of medical images helps in checking the growth of disease like tumour, controlling the dosage of medicine, and dosage of exposure to radiations. Medical image segmentation is really a challenging task due to the various artefacts present in the images. Recently, deep neural models have shown application in various image segmentation tasks. This significant growth is due to the achievements and high performance of the deep learning strategies. This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks. The paper examines the various widely used medical image datasets, the different metrics used for evaluating the segmentation tasks, and performances of different CNN based networks. In comparison to the existing review and survey papers, the present work also discusses the various challenges in the field of segmentation of medical images and different state-of-the-art solutions available in the literature.</jats:p>

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