Medical image segmentation with 3D convolutional neural networks: A survey

dc.contributor.authorNiyas, S.
dc.contributor.authorPawan, S.J.
dc.contributor.authorAnand Kumar, M.A.
dc.contributor.authorRajan, J.
dc.date.accessioned2026-02-08T18:38:44Z
dc.date.issued2022
dc.description.abstractComputer-aided medical image analysis plays a significant role in assisting medical practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present, convolutional neural networks (CNNs) are the preferred choice for medical image analysis. In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods are gaining popularity in medical image analysis. Here, we present an extensive review of the recently proposed 3D deep learning methods for medical image segmentation. Furthermore, the research gaps and future directions in 3D medical image segmentation are discussed. © 2022 Elsevier B.V.
dc.identifier.citationNeurocomputing, 2022, Vol.493, , p.397 -413
dc.identifier.issn9252312
dc.identifier.urihttps://doi.org/10.1016/j.neucom.2022.04.065
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/34316
dc.publisherElsevier B.V.
dc.subject3D deep learning
dc.subjectConvolutional Neural Networks
dc.subjectMedical image analysis
dc.titleMedical image segmentation with 3D convolutional neural networks: A survey

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