3D Deep Learning on Medical Images: A Review (2004.00218v4)

Abstract: The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

Insights into 3D Deep Learning on Medical Images

The review paper provides a comprehensive analysis of the deployment and advancement of three-dimensional (3D) deep learning methods specifically applied to medical imaging. This domain has seen significant evolution largely due to the availability of advanced imaging technology and high-capacity computational frameworks, with particular emphasis on convolutional neural networks (CNNs). This paper delineates the trajectory, current applications, and challenges of 3D CNNs as it pertains to medical scenarios such as classification, segmentation, detection, and localization tasks.

Development Trajectory

The history underscores the adaptation of CNNs from simpler, two-dimensional applications to complex, volumetric medical data. Since the landmark development of models like AlexNet, the medical imaging community has benefited significantly from employing CNNs capable of processing 3D data. The authors provide a solid foundation by describing the mathematical underpinnings of 3D CNNs and the critical preprocessing steps necessary for medical data to ensure optimal model performance. The architectures of CNN such as the AlexNet, GoogLeNet, and various incipient versions highlight critical improvements via deeper and more computationally efficient networks, each with specific advancements tailored toward handling extensive data inputs efficiently.

Key Applications

The paper categorizes the applications of 3D CNNs into several focal areas:

1. Segmentation: The segmentation of lesions, tumors, and other anatomical structures represents one of the cardinal utilities of 3D CNNs. Initial architectures like DeepMedic have demonstrated valuable contributions to automatic brain lesion segmentation. Variants like the U-Net, with its 3D adaptations, show how modifications in architecture have advanced the precision and efficiency of automated segmentation.
2. Classification: The classification of diseases such as Alzheimer’s Disease through 3D CNNs reveals profound implications in the early detection and management of neurodegenerative disorders. Networks designed to handle volumetric data from MRI, functional MRI, and others have demonstrated robustness in effectively identifying pathological presentations.
3. Detection & Localization: The identification and spatial pinpointing of medical anomalies —such as cerebral microbleeds using 3D architectures— illustrate the capacity of CNNs to enhance radiological assessments by reducing false positives. Applications span beyond neurological assessments to other body systems which demand high precision and reliability, such as lung nodule detection.
4. Registration: The alignment of multimodal imaging datasets remains a burgeoning area enabled by 3D deep learning, facilitating improved comparative analysis and effective diagnostic protocols. Techniques promoting the automatic alignment of datasets with noted abnormalities have potential extensions in personalized medicine.

Challenges and Future Directions

While 3D deep learning introduces enhanced models with superior accuracy for medical imaging contexts, challenges persist. The computational demands remain formidable, particularly concerning model size and memory requirements, dictating the need for novel methods to down-sample data efficiently without losing pertinent information. Furthermore, data variability among patient demographics and imaging protocols poses an interpretability challenge for these neural networks, particularly in ensuring that learned features from extensive datasets are generalizable and applicable in diverse clinical settings.

The necessity for large datasets introduces impediments due to the often limited availability of labeled medical images, combined with high costs in acquiring these datasets. Approaches like transfer learning and data augmentation, including methods such as generative adversarial networks (GANs), present potential solutions.

In conclusion, 3D CNNs herald a transformative phase in medical imaging, promising increased accuracy and efficiency in diagnostic practices. Continued advancements in computational approaches and strategies to address present limitations will likely yield richer applications across increasingly complex medical scenarios. These advancements, while currently focused on specific domains and applications, lay the groundwork for expansive growth and integration within clinical frameworks, enabling enhanced patient outcomes through refined diagnostic capabilities.