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A recent review and a taxonomy for hard and soft tissue visualization‐based mixed reality
Author(s) -
Tuladhar Selina,
AlSallami Nada,
Alsadoon Abeer,
Prasad P.W.C.,
Alsadoon Omar H.,
Haddad Sami,
Alrubaie Ahmad
Publication year - 2020
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.2120
Subject(s) - visualization , computer science , taxonomy (biology) , popularity , human–computer interaction , information retrieval , data science , artificial intelligence , biology , psychology , social psychology , botany
Abstract Background Mixed reality (MR) visualization is gaining popularity in image‐guided surgery (IGS) systems, especially for hard and soft tissue surgeries. However, a few MR systems are implemented in real time. Some factors are limiting MR technology and creating a difficulty in setting up and evaluating the MR system in real environments. Some of these factors include: the end users are not considered, the limitations in the operating room, and the medical images are not fully unified into the operating interventions. Methodology The purpose of this article is to use Data, Visualization processing, and View (DVV) taxonomy to evaluate the current MR systems. DVV includes all the components required to be considered and validated for the MR used in hard and soft tissue surgeries. This taxonomy helps the developers and end users like researchers and surgeons to enhance MR system for the surgical field. Results We evaluated, validated, and verified the taxonomy based on system comparison, completeness, and acceptance criteria. Around 24 state‐of‐the‐art solutions that are picked relate to MR visualization, which is then used to demonstrate and validate this taxonomy. The results showed that most of the findings are evaluated and others are validated. Conclusion The DVV taxonomy acts as a great resource for MR visualization in IGS. State‐of‐the‐art solutions are classified, evaluated, validated, and verified to elaborate the process of MR visualization during surgery. The DVV taxonomy provides the benefits to the end users and future improvements in MR.