<?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>S0188-95322025000200001</article-id>
<article-id pub-id-type="doi">10.17488/rmib.46.2.1462</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Pix2Pix Generative Adversarial Network for Cellular Nuclei and Cytoplasm Segmentation on Pap Smear Images]]></article-title>
<article-title xml:lang="es"><![CDATA[Red Generativa Antagónica Pix2Pix para la Segmentación de Núcleos Celulares y Citoplasma en Imágenes de Frotis de Papanicolaou]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castro Cortés]]></surname>
<given-names><![CDATA[Francisco Javier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[Carlos Eric]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Acosta Cruz]]></surname>
<given-names><![CDATA[Erika]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Celaya-Padilla]]></surname>
<given-names><![CDATA[José M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Zacatecas  ]]></institution>
<addr-line><![CDATA[ Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Coahuila  ]]></institution>
<addr-line><![CDATA[ Coahuila]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2025</year>
</pub-date>
<volume>46</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322025000200001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322025000200001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322025000200001&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In medical imaging for Pap smear tests, accurately identifying regions of interest, such as the nucleus and cytoplasm, remains a critical challenge due to the complex morphology and overlapping structures in cervical cell images. This complexity increases the risk of misidentification, potentially leading to false positives in computer-assisted diagnosis. To address this issue, this study introduces a novel approach by developing and evaluating a framework for the precise segmentation of nuclei and cytoplasm in cervical cell images using a cGAN-based model, Pix2Pix, applied to a dataset validated by specialists. The generated images are compared with target images, converted to binary, and an AND operation is performed to evaluate pixel overlap in the areas of interest. The evaluation metrics highlight a segmentation accuracy of 88.8 % and sensitivity of 89.62 % for nuclei, while for cytoplasm, precision reached 89.62 % and sensitivity 99.34 %. The Jaccard indices were 80.89 % for nuclei and 96.71 % for cytoplasm. These results demonstrate the effectiveness of the model in segmenting nuclei and cytoplasm in cervical cells.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En el campo de las imágenes médicas para la prueba de Papanicolaou, identificar con precisión las regiones de interés, como el núcleo y el citoplasma, sigue siendo un desafío crítico debido a la compleja morfología y las estructuras superpuestas en las imágenes de células cervicales. Esta complejidad aumenta el riesgo de identificaciones erróneas, lo que podría llevar a falsos positivos en el diagnóstico asistido por computadora. Para abordar este problema, este estudio presenta un enfoque novedoso mediante el desarrollo y evaluación de un marco para la segmentación precisa de núcleos y citoplasmas en imágenes de células cervicales, utilizando un modelo basado en cGAN, Pix2Pix, aplicado a un conjunto de datos validado por especialistas. Las imágenes generadas se compararon con las imágenes objetivo, se convirtieron a formato binario y se realizó una operación AND para evaluar la superposición de píxeles en las áreas de interés. Las métricas de evaluación destacaron una precisión de segmentación del 88.8 % y una sensibilidad del 89.62 % para los núcleos, mientras que, para el citoplasma, la precisión alcanzó el 89.62 % y la sensibilidad el 99.34 %. Los índices de Jaccard fueron del 80.89 % para los núcleos y del 96.71 % para el citoplasma. Estos resultados demuestran la efectividad del modelo en la segmentación de núcleos y citoplasmas en células cervicales.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[cancer]]></kwd>
<kwd lng="en"><![CDATA[cGAN]]></kwd>
<kwd lng="en"><![CDATA[segmentation]]></kwd>
<kwd lng="en"><![CDATA[PAP]]></kwd>
<kwd lng="en"><![CDATA[Pix2Pix]]></kwd>
<kwd lng="es"><![CDATA[cáncer]]></kwd>
<kwd lng="es"><![CDATA[cGAN]]></kwd>
<kwd lng="es"><![CDATA[segmentación]]></kwd>
<kwd lng="es"><![CDATA[PAP]]></kwd>
<kwd lng="es"><![CDATA[Pix2Pix]]></kwd>
</kwd-group>
</article-meta>
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