Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey
It provides a comprehensive review for researchers and practitioners in medical image analysis, but is incremental as it synthesizes existing work.
This paper surveys recent 3D deep learning methods for medical image segmentation, identifying research gaps and future directions in the field.
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.