<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322024000300020</article-id>
<article-id pub-id-type="doi">10.17488/rmib.45.3.2</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[UMInSe: An Unsupervised Method for Segmentation and Detection of Surgical Instruments based on K-means]]></article-title>
<article-title xml:lang="es"><![CDATA[UMInSe: Método no Supervisado para la Segmentación y Detección de Instrumentos Quirúrgicos Basado en K-means]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arevalo-Ancona]]></surname>
<given-names><![CDATA[Rodrigo Eduardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Haro-Mendoza]]></surname>
<given-names><![CDATA[Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cedillo-Hernandez]]></surname>
<given-names><![CDATA[Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gonzalez-Villela]]></surname>
<given-names><![CDATA[Victor J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional  ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Autónoma de México  ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>45</volume>
<numero>3</numero>
<fpage>20</fpage>
<lpage>50</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322024000300020&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322024000300020&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322024000300020&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Surgical instrument segmentation in images is crucial for improving precision and efficiency in surgery, but it currently relies on costly and labor-intensive manual annotations. An unsupervised approach is a promising solution to this challenge. This paper introduces a surgical instrument segmentation method using unsupervised machine learning, based on the K-means algorithm, to identify Regions of Interest (ROI) in images and create the image ground truth for neural network training. The Gamma correction adjusts image brightness and enhances the identification of areas containing surgical instruments. The K-means algorithm clusters similar pixels and detects ROIs despite changes in illumination, yielding an efficient segmentation despite variations in image illumination and obstructing objects. Therefore, the neural network generalizes the image features learning for instrument segmentation in different tasks. Experimental results using the JIGSAWS and EndoVis databases demonstrate the method's effectiveness and robustness, with a minimal error (0.0297) and high accuracy (0.9602). These results underscore the precision of surgical instrument detection and segmentation, which is crucial for automating instrument detection in surgical procedures without pre-labeled datasets. Furthermore, this technique could be applied in surgical applications such as surgeon skills assessment and robot motion planning, where precise instrument detection is indispensable.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La segmentación de instrumentos quirúrgicos en imágenes es crucial para mejorar la precisión y eficiencia en cirugía, pero actualmente depende de anotaciones manuales costosas y laboriosas. Un enfoque no supervisado es una solución prometedora para este desafío. Este artículo introduce un método de segmentación de instrumentos quirúrgicos utilizando aprendizaje automático no supervisado, basado en el algoritmo K-means, para identificar Regiones de Interés (ROI) en imágenes y crear el ground truth de las imágenes para el entrenamiento de redes neuronales. La corrección Gamma ajusta el brillo de la imagen y mejora la identificación de áreas que contienen instrumentos quirúrgicos. El algoritmo K-means agrupa píxeles similares y detecta las ROI a pesar de los cambios en la iluminación, logrando una segmentación eficiente a pesar de las variaciones en la iluminación de la imagen y los objetos obstructores. Por lo tanto, la red neuronal generaliza el aprendizaje de las características de la imagen para la segmentación de instrumentos en diferentes tareas. Los resultados experimentales utilizando las bases de datos JIGSAWS y EndoVis demuestran la efectividad y robustez del método, con un error mínimo (0.0297) y alta precisión (0.9602). Estos resultados subrayan la precisión en la detección y segmentación de instrumentos quirúrgicos, lo cual es crucial para automatizar la detección de instrumentos en procedimientos quirúrgicos sin conjuntos de datos preetiquetados. Además, esta técnica podría aplicarse en aplicaciones quirúrgicas como la evaluación de habilidades del cirujano y la planificación de movimientos de robots, donde la detección precisa de instrumentos es indispensable.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[JIGSAWS database]]></kwd>
<kwd lng="en"><![CDATA[K-means]]></kwd>
<kwd lng="en"><![CDATA[surgical instruments segmentation]]></kwd>
<kwd lng="en"><![CDATA[unsupervised segmentation]]></kwd>
<kwd lng="es"><![CDATA[base de datos JIGSAWS]]></kwd>
<kwd lng="es"><![CDATA[K-means]]></kwd>
<kwd lng="es"><![CDATA[segmentación instrumentos quirúrgicos]]></kwd>
<kwd lng="es"><![CDATA[segmentación no supervisada]]></kwd>
</kwd-group>
</article-meta>
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