Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey

TL;DR

This survey reviews recent advances in 3D convolutional neural networks for medical image segmentation, highlighting their growing importance due to advancements in 3D imaging and hardware, and discusses future research directions.

Contribution

It provides an extensive overview of current 3D deep learning methods in medical image segmentation and identifies research gaps and future challenges.

Findings

3D CNNs are increasingly used in medical image segmentation.

Advancements in hardware support large-scale 3D data processing.

The survey highlights key research gaps and future directions.

Abstract

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.