  Computer-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 (CNN) 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 evolved 3D deep learning methods
in medical image segmentation. Furthermore, the research gaps and future
directions in 3D medical image segmentation are discussed.
