A methodology for peripheral nerve segmentation using a multiple annotators approach based on Centered Kernel Alignment

dc.contributor.advisor Orozco Gutiérrez, Álvaro Ángel spa
dc.contributor.author Gil González, Julián spa
dc.creator.degree Magíster en Ingeniería Eléctrica spa
dc.date.accessioned 2016-12-14T16:58:33Z
dc.date.accessioned 2021-11-02T20:34:11Z
dc.date.available 2016-12-14T16:58:33Z
dc.date.available 2021-11-02T20:34:11Z
dc.date.issued 2016
dc.description.abstract Peripheral Nerve Blocking (PNB) is a technique commonly used to perform regional anesthesia and for pain management. The success of PNB procedures depends on the accurate location of the target nerve. Recently, ultrasound imaging has been widely used to locate nerve structures to carry out PNB, due to it enables a non-invasive visualization of the target nerve and the anatomical structures around it. However, the ultrasound images are affected by several artifacts making difficult the accurate delimitation of nerves. In the literature, several approaches have been proposed to carry out automatic or semi-automatic segmentation. Nevertheless, these methods are designed assuming that the gold standard is available, and for this segmentation problem this gold standard can not be obtained considering that it corresponds to subjective interpretation. In this sense, for building those segmentation models, we do not have access to the actual label but an amount of subjective annotations provided by multiple experts. To deal with this drawback we use the concepts of a relatively new area of machine learning known as “Learning from crowds”, this area deals with supervised learning problems considering the case when the gold standard is not available. In this project, we develop a nerve segmentation system that includes: a preprocessing stage, feature extraction methodology based on adaptive methods, and a Centered Kernel Alignment (CKA) based representation to measure the annotators performance for building a classifier with multiple annotators in order to support peripheral nerve segmentation. Our approach to classification with multiple annotators based on CKA is tested on both simulated data and real data; similarly, the methodology of automatic segmentation proposed in this work was tested over ultrasound images labeled by a set of specialists who give their opinion about the location of nerve structures. According to the results, we conclude that our methodology can be used to locate nerve structures in ultrasound images even if the gold standard (the actual location of nerve structures) is not available in the training stage. Moreover, we determine that the approach proposed in this work could be implemented as a guiding tool for the anesthesiologist to carry out PNB procedures assisted by ultrasound imaging. spa
dc.format application/pdf spa
dc.identifier.local T621.367 G463;6310000118097 F4792 spa
dc.identifier.uri https://hdl.handle.net/11059/7255
dc.language.iso spa spa
dc.publisher Pereira : Universidad Tecnológica de Pereira spa
dc.publisher.department Facultad de Ingenierías Eléctrica, Electrónica y Ciencias de la Computación spa
dc.publisher.program Maestría en Ingeniería Eléctrica spa
dc.rights Attribution-NonCommercial-Noderivatives 4.0 International *
dc.rights EL AUTOR, manifiesta que la obra objeto de la presente autorización es original y la realizó sin violar o usurpar derechos de autor de terceros, por lo tanto la obra es de exclusiva autoría y tiene la titularidad sobre la misma. PARÁGRAFO: En caso de presentarse cualquier reclamación o acción por parte de un tercero en cuanto a los derechos de autor sobre la obra en cuestión, EL AUTOR, asumirá toda la responsabilidad, y saldrá en defensa de los derechos aquí autorizados; para todos los efectos la universidad actúa como un tercero de buena fe. EL AUTOR, autoriza a LA UNIVERSIDAD TECNOLOGICA de PEREIRA, para que en los términos establecidos en la Ley 23 de 1982, Ley 44 de 1993, decisión andina 351 de 1993, decreto 460 de 1995 y demás normas generales sobre la materia, utilice y use la obra objeto de la presente autorización. spa
dc.rights.accessRights openAccess spa
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Nervios periféricos spa
dc.subject Ultrasonido en medicina spa
dc.subject Procesamiento de datos - Técnicas digitales spa
dc.title A methodology for peripheral nerve segmentation using a multiple annotators approach based on Centered Kernel Alignment spa
dc.type masterThesis spa
dc.type.hasVersion acceptedVersion spa
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