Literature DB >> 29190576

The semiotics of medical image Segmentation.

John S H Baxter1, Eli Gibson2, Roy Eagleson3, Terry M Peters4.   

Abstract

As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical image segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive interpretation of the computer as a symbol processing machine. Using the perspective of the computer as dialogue partner, we can motivate the semiotic understanding of medical image segmentation. Taking advantage of Peircean semiotic traditions and new philosophical inquiry into the structure and quality of metaphors, we can construct a unified framework for the interpretation of medical image segmentation as a sign exchange in which each sign acts as an interface metaphor. This allows for a notion of finite semiosis, described through a schematic medium, that can rigorously describe how clinicians and computers interpret the signs mediating their interaction. Altogether, this framework provides a unified approach to the understanding and development of medical image segmentation interfaces.
Copyright © 2017 Elsevier B.V. All rights reserved.

Keywords:  Human computer interaction; Interface metaphors; Medical image segmentation; Peircean semiotics

Mesh:

Year:  2017        PMID: 29190576     DOI: 10.1016/j.media.2017.11.007

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  2 in total

1.  LinSEM: Linearizing segmentation evaluation metrics for medical images.

Authors:  Jieyu Li; Jayaram K Udupa; Yubing Tong; Lisheng Wang; Drew A Torigian
Journal:  Med Image Anal       Date:  2019-11-09       Impact factor: 8.545

2.  Combining simple interactivity and machine learning: a separable deep learning approach to subthalamic nucleus localization and segmentation in MRI for deep brain stimulation surgical planning.

Authors:  John S H Baxter; Pierre Jannin
Journal:  J Med Imaging (Bellingham)       Date:  2022-07-11
  2 in total

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