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- W3024713218 endingPage "236" @default.
- W3024713218 startingPage "213" @default.
- W3024713218 abstract "The analysis of interventional images is a topic of high interest for the medical-image analysis community. Such an analysis may provide interventional-medicine professionals with both decision support and context awareness, with the final goal of improving patient safety. The aim of this chapter is to give an overview of some of the most recent approaches (up to 2018) in the field, with a focus on Convolutional Neural Networks (CNNs) for both segmentation and classification tasks. For each approach, summary tables are presented reporting the used dataset, involved anatomical region and achieved performance. Benefits and disadvantages of each approach are highlighted and discussed. Available datasets for algorithm training and testing and commonly used performance metrics are summarized to offer a source of information for researchers that are approaching the field of interventional-image analysis. The advancements in deep learning for medical-image analysis are involving more and more the interventional-medicine field. However, these advancements are undeniably slower than in other fields (e.g. preoperative-image analysis) and considerable work still needs to be done in order to provide clinicians with all possible support during interventional-medicine procedures." @default.
- W3024713218 created "2020-05-21" @default.
- W3024713218 creator A5000889718 @default.
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- W3024713218 creator A5083456765 @default.
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- W3024713218 creator A5091783140 @default.
- W3024713218 date "2020-01-01" @default.
- W3024713218 modified "2023-09-26" @default.
- W3024713218 title "Supervised CNN Strategies for Optical Image Segmentation and Classification in Interventional Medicine" @default.
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- W3024713218 doi "https://doi.org/10.1007/978-3-030-42750-4_8" @default.