Risk map generation for keyhole neurosurgery using fuzzy logic for trajectory evaluation

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• 作者：Alejandro De Leó ; n-CuevasAuthor Vitae ; Saú ; l Tovar-Arriaga ; ^{saulotov@yahoo.com.mx}Author Vitae ; Arturo Gonzá ; lez-Gutié ; rrezAuthor Vitae ; Marco Antonio Aceves-Ferná ; ndezAuthor Vitae
• 关键词：Automatic trajectory planning ; Keyhole neurosurgery ; Artificial intelligence in surgery ; Fuzzy logic
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：12 April 2017
• 年：2017
• 卷：233
• 期：Complete
• 页码：81-89
• 全文大小：1541 K
• 卷排序：233

文摘

Planning safe trajectories in keyhole neurosurgery requires a high level of accuracy in order to reach small anatomical structures in operations such as biopsies, deep brain stimulation and other therapy/diagnostic procedures. In this study, we propose a user interface that assists the neurosurgeon to select the entry point of the procedure using an atlas which displays the risk of the different linear trajectories from the head surface skin to the target. The risk assignment is carried out in two main steps, a weighted sum which depends on the distance of a single voxel to other risk structures and a second criteria using fuzzy rules to take into account other qualities such as length, which is the main contribution of this work. As a result, we present risk maps of two targets and visualize the less risky trajectories according to the system.