Research Area: Machine Learning
Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical images. They have been used extensively for medical image segmentation as the first and significant components of the diagnosis and treatment pipeline. Medical image segmentation is efficiently addressed by many types of deep neural networks, such as convolutional neural networks, fully convolutional network recurrent networks, adversarial networks, and U-shaped networks. This paper reviews the major DL models and applications pertinent to medical image segmentation and summarizes over 150 contributions to the field. Brief overviews of articles are provided by application area: anatomical structures such as organs, bones, and vessels, and abnormalities such as lesions and calcification. Moreover, we discuss current challenges and suggest directions for future research.
Keywords:
Deep Learning
Medical Images Segmentation
Author(s) Name: Manar Aljabri, Manal AlGhamdi
Journal name: Neurocomputing
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.neucom.2022.07.070
Volume Information: Volume 506, 28 September 2022, Pages 311-335
