page_title: Computational anatomy
section_title: The Bayesian model of computational anatomy
hierarchical_section_title: Computational anatomy / The Bayesian model of computational anatomy
caption_attribution_description: English: sourch-channel-modell-Shannon
context_page_description: Computational anatomy is an interdisciplinary field of biology focused on quantitative investigation and modelling of anatomical shapes variability. It involves the development and application of mathematical, statistical and data-analytical methods for modelling and simulation of biological structures.
  The field is broadly defined and includes foundations in anatomy, applied mathematics and pure mathematics, machine learning, computational mechanics, computational science, biological imaging, neuroscience, physics, probability, and statistics; it also has strong connections with fluid mechanics and geometric mechanics. Additionally, it complements newer, interdisciplinary fields like bioinformatics and neuroinformatics in the sense that its interpretation uses metadata derived from the original sensor imaging modalities. It focuses on the anatomical structures being imaged, rather than the medical imaging devices. It is similar in spirit to the history of Computational linguistics, a discipline that focuses on the linguistic structures rather than the sensor acting as the transmission and communication media.
context_section_description: The central statistical model of computational anatomy in the context of medical imaging has been the source-channel model of Shannon theory; the source is the deformable template of images , the channel outputs are the imaging sensors with observables  (see Figure).
  See The Bayesian model of computational anatomy for discussions (i) MAP estimation with multiple atlases, (ii)
  MAP segmentation with multiple atlases, MAP estimation of templates from populations.
caption: Source-channel model showing the source of images the deformable template 
    
      
        
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   and channel output associated with MRI sensor 
    
      
        
          
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