Image‐ versus histogram‐based considerations in semantic segmentation of pulmonary hyperpolarized gas images

• Published in: Magnetic resonance in medicine Vol. 86; no. 5; pp. 2822 - 2836
• Main Authors: Tustison, Nicholas J., Altes, Talissa A., Qing, Kun, He, Mu, Miller, G. Wilson, Avants, Brian B., Shim, Yun M., Gee, James C., Mugler, John P., Mata, Jaime F.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.11.2021
• Subjects: Advanced Normalization Tools; Algorithms; Artificial neural networks; Computer applications; convolutional neural network; Deep learning; Domains; Ecosystem; Fields (mathematics); functional lung imaging; Histograms; Humans; Image processing; Image Processing, Computer-Assisted; Image segmentation; Lung - diagnostic imaging; Lungs; Machine learning; Magnetic Resonance Imaging; Neural networks; Optimization; Probabilistic models; Retrospective Studies; segmentation; Semantic segmentation; Semantics; Spatial data; Xenon 129; Xenon Isotopes; deep learning; Advanced Normalization Tools; functional lung imaging; segmentation; convolutional neural network

Purpose To characterize the differences between histogram‐based and image‐based algorithms for segmentation of hyperpolarized gas lung images. Methods Four previously published histogram‐based segmentation algorithms (ie, linear binning, hierarchical k‐means, fuzzy spatial c‐means, and a Gaussian mi...