3D-based Convolutional Neural Networks for Medical Image Segmentation: A Review

Abstract

Medical image segmentation is essential for disease screening and diagnosis, particularly through techniques like anatomical and lesion segmentation that can be used to isolate critical regions of interest. However, manual segmentation is labor-intensive, costly, and susceptible to subjective bias, underscoring the need for automation. Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced segmentation accuracy and efficiency. With the introduction of 3D imaging, research has evolved from 2D CNNs to 3D CNNs, which leverage inter-slice information to improve segmentation precision. This paper aims to provide a literature review of studies published between 2018 and 2024 on platforms such as Google Scholar and ScienceDirect, where the identified relevant research are "3D segmentation" and "3D medical imaging". This study outlines the key stages of 3D CNN segmentation that include preprocessing, region-of-interest extraction, and post-processing. Furthermore, this study emphasizes the application of 3D CNN architectures to complex lung imaging scenarios, such as lung cancer and COVID-19. Although 3D CNNs outperform 2D CNNs in preserving spatial continuity across slices, they present notable limitations. Key challenges include heavy computational and high memory demands, as well as a dependency on large annotated datasets, which are often scarce in medical imaging. Additionally, effective multiscale feature learning remains a challenging issue, with current architectures struggling to generalize the features of interest across several usage variations. To further improve the segmentation performance, future research should prioritize developing adaptive algorithms and fostering interdisciplinary collaboration between computer scientists and medical professionals to design efficient and scalable models, designed specifically for clinical applications. This future research direction will enhance diagnostic accuracy and segmentation quality in 3D medical imaging.