http://www.rmib.com.mx/index.php/rmib/issue/feedRevista Mexicana de Ingenieria Biomedica2025-05-01T00:00:00+00:00Prof. Dora-Luz Floresrib.somib@gmail.comOpen Journal Systems<center> <p><strong>MISSION</strong></p> <p align="left"><em>La Revista Mexicana de Ingeniería Biomédica</em> (The Mexican Journal of Biomedical Engineering, RMIB, for its Spanish acronym) is a publication oriented to the dissemination of papers of the Mexican and international scientific community whose lines of research are aligned to the improvement of the quality of life through engineering techniques.</p> <p align="left">The papers that are considered for being published in the RMIB must be original, unpublished, and first rate, and they can cover the areas of Medical Instrumentation, Biomedical Signals, Medical Information Technology, Biomaterials, Clinical Engineering, Physiological Models, and Medical Imaging as well as lines of research related to various branches of engineering applied to the health sciences.</p> <p align="left">The RMIB is an electronic publication continuously released since 2020, structured into three volumes (January, May, September) by the Mexican Society of Biomedical Engineering, founded since 1979. It publishes articles in spanish and english and is aimed at academics, researchers and professionals interested in the subspecialties of Biomedical Engineering.</p> <p><strong>INDEXES</strong></p> <p><em>La Revista Mexicana de Ingeniería Biomédica</em> is a quarterly publication, and it is found in the following indexes:</p> <p><img src="https://www.rmib.mx/public/site/images/administrador/índices_y_repositorios_(1100_×_1000 px).jpg" /></p> </center>http://www.rmib.com.mx/index.php/rmib/article/view/1462Pix2Pix Generative Adversarial Network for Cellular Nuclei and Cytoplasm Segmentation on Pap Smear Images2025-02-28T16:24:21+00:00Francisco Javier Castro Cortésjavier.castro@uaz.edu.mxCarlos Eric Galván Tejadaericgalvan@uaz.edu.mxErika Acosta Cruzerika.acosta@uadec.edu.mxJosé María Celaya Padillajose.celaya@uaz.edu.mx<div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>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> </div> </div> </div>2025-05-01T00:00:00+00:00Copyright (c)