Medical Imaging Overview

Principles

Definition

Medical imaging is a research field and methodological approach dedicated to the creation of visual representations of the internal structures and physiological processes of living organisms. It encompasses a range of technologies employed to acquire non-invasive or minimally invasive anatomical and functional data, critical for diagnosis, treatment planning, and biomedical research.

Ontological type

Core Modalities

Clinical Applications

Image Reconstruction

Key developments

Key Figures

Imaging Physics Foundations era

Harvey Feigenbaum [1] was a pioneering figure in Imaging Physics Foundations, with affiliations including Indiana University – Purdue University Indianapolis [3] and Indiana University School of Medicine [4]. His key contributions in this era include pioneering quantitative ultrasound assessments of the left ventricle, as illustrated by Ultrasound Measurements of the Left Ventricle [7] and Left Ventricular Wall Thickness Measured by Ultrasound [8], which translated raw ultrasound signals into reproducible functional metrics essential for clinical imaging workflows. H. William Strausś [2] is associated with Johns Hopkins University [5] and McMaster University [6] during this era. His 1971 paper A Noninvasive Scintiphotographic Method for Detecting Regional Ventricular Dysfunction in Man [9] represents a key contribution to noninvasive nuclear imaging of regional function, enabling earlier functional assessment and broader clinical adoption.

Quantitative Computational Imaging era

James G. Fujimoto[1] has been associated with Harvard University[3] and the Massachusetts Institute of Technology[4] during this era. The Optical Coherence Tomography[5] paper introduced OCT, a noninvasive imaging modality providing micrometer-scale cross-sectional resolution that enabled quantitative measurements of tissue structure and function, a foundational contribution for this era. Joel S. Schuman[2] has been associated with Harvard University[3] and the Massachusetts Institute of Technology[4] during this era. His co-authored Optical Coherence Tomography[5] work helped establish OCT as a foundational technology for cross-sectional, quantitative imaging, enabling measurement of fine tissue detail and supporting the standards, validation frameworks, and multi-site comparability characteristic of the era.

Deep Learning Segmentation era

Klaus Maier‑Hein [1] is a prominent figure in the Deep Learning Segmentation era, with affiliations to Harvard University [3] and the University of California, San Francisco [4] during this period. Maier‑Hein [1] has key contributions in this era including co‑developing nnU-Net [7], a self‑configuring method for deep learning‑based biomedical image segmentation that reduces manual tuning and enables rapid adaptation across datasets. Fabian Isensee [2] is associated with Johns Hopkins University [5] and Heidelberg University [6] during this era. Isensee [2] contributed to the nnU-Net [7] framework, enabling standardized, self‑configuring segmentation pipelines that accelerate cross‑dataset adaptation and reproducibility, thereby supporting scalable translation of segmentation methods into clinical research.