Método de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificial

ilustraciones, gráficas, tablas

Autores:: Duque Miranda, Juan Esteban

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Método de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificial
dc.title.translated.eng.fl_str_mv	Segmentation of the prostate in magnetic resonance images using artificial intelligence techniques
title	Método de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificial
spellingShingle	Método de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificial 000 - Ciencias de la computación, información y obras generales 610 - Medicina y salud Inteligencia artificial Artificial intelligence Tecnología médica Medical technology Segmentación de la próstata Resonancia magnética Compuerta de atención Prostate segmentation magnetic resonance Unet attention gate Resnet
title_short	Método de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificial
title_full	Método de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificial
title_fullStr	Método de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificial
title_full_unstemmed	Método de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificial
title_sort	Método de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificial
dc.creator.fl_str_mv	Duque Miranda, Juan Esteban
dc.contributor.advisor.none.fl_str_mv	Branch Bedoya, John Willian Ospina Arango, Juan David
dc.contributor.author.none.fl_str_mv	Duque Miranda, Juan Esteban
dc.contributor.researchgroup.spa.fl_str_mv	Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales 610 - Medicina y salud
topic	000 - Ciencias de la computación, información y obras generales 610 - Medicina y salud Inteligencia artificial Artificial intelligence Tecnología médica Medical technology Segmentación de la próstata Resonancia magnética Compuerta de atención Prostate segmentation magnetic resonance Unet attention gate Resnet
dc.subject.lemb.none.fl_str_mv	Inteligencia artificial Artificial intelligence Tecnología médica Medical technology
dc.subject.proposal.spa.fl_str_mv	Segmentación de la próstata Resonancia magnética Compuerta de atención
dc.subject.proposal.eng.fl_str_mv	Prostate segmentation magnetic resonance Unet attention gate Resnet
description	ilustraciones, gráficas, tablas
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-12-09
dc.date.accessioned.none.fl_str_mv	2022-03-10T15:19:59Z
dc.date.available.none.fl_str_mv	2022-03-10T15:19:59Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
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status_str	acceptedVersion
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
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url	https://repositorio.unal.edu.co/handle/unal/81179 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Aldoj, N., Biavati, F., Michallek, F., Stober, S., & Dewey, M. (2020). Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net. Scientific Reports 2020 10:1, 10(1), 1–17. https://doi.org/10.1038/s41598-020-71080-0 American Cancer Society (ACS). (s.f.). Key Statistics for Prostate Cancer. https://www.cancer.org/cancer/prostate-cancer/about/key-statistics.html#written_by Bravo, L. E., & Muñoz, N. (2018). Epidemiology of cancer in Colombia. Colombia Médica, 49(1), 9–12. https://doi.org/10.25100/CM.V49I1.3877 Chen, S., Ma, K., & Zheng, Y. (2019). Med3D: Transfer Learning for 3D Medical Image Analysis. https://doi.org/10.48550/arxiv.1904.00625 Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9901 LNCS, 424–432. https://doi.org/10.48550/arxiv.1606.06650 Comelli, A., Dahiya, N., Stefano, A., Vernuccio, F., Portoghese, M., Cutaia, G., Bruno, A., Salvaggio, G., & Yezzi, A. (2021). Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging. Applied Sciences 2021, Vol. 11, Page 782, 11(2), 782. https://doi.org/10.3390/APP11020782 Boor, C. (1972). On calculating with B-splines. Journal of Approximation Theory, 6(1), 50–62. https://doi.org/10.1016/0021-9045(72)90080-9 Ghavami, N., Hu, Y., Gibson, E., Bonmati, E., Emberton, M., Moore, C. M., & Barratt, D. C. (2019). Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration. Medical Image Analysis, 58, 101558. https://doi.org/10.1016/J.MEDIA.2019.101558 Guo, Y., Gao, Y., & Shen, D. (2016). Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching. IEEE Transactions on Medical Imaging, 35(4), 1077–1089. https://doi.org/10.1109/TMI.2015.2508280 He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. https://doi.org/10.48550/arxiv.1512.03385 Isensee, F., Jäger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2020). Automated Design of Deep Learning Methods for Biomedical Image Segmentation. Nature Methods, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-z Jia, H., Song, Y., Huang, H., Cai, W., & Xia, Y. (2019). HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11765 LNCS, 110–118. https://doi.org/10.1007/978-3-030-32245-8_13 Jia, H., Xia, Y., Song, Y., Zhang, D., Huang, H., Zhang, Y., & Cai, W. (2019). 3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images. IEEE Transactions on Medical Imaging, 39(2), 447–457. https://doi.org/10.1109/TMI.2019.2928056 Khan, Z., Yahya, N., Alsaih, K., Al-Hiyali, M. I., & Meriaudeau, F. (2021). Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review. IEEE Access, 9, 97878–97905. https://doi.org/10.1109/ACCESS.2021.3090825 Klein, S., van der Heide, U. A., Lips, I. M., van Vulpen, M., Staring, M., & Pluim, J. P. W. (2008). Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics, 35(4), 1407–1417. https://doi.org/10.1118/1.2842076 Larsen, C. T., Eugenio Iglesias, J., & van Leemput, K. (2014). N3 bias field correction explained as a Bayesian modeling method. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8677, 1–12. https://doi.org/10.1007/978-3-319-12289-2_1 Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., Strand, R., Malmberg, F., Ou, Y., Davatzikos, C., Kirschner, M., Jung, F., Yuan, J., Qiu, W., Gao, Q., … Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2), 359–373. https://doi.org/10.1016/J.MEDIA.2013.12.002 Mahapatra, D., & Buhmann, J. M. (2014). Prostate MRI segmentation using learned semantic knowledge and graph cuts. IEEE Transactions on Biomedical Engineering, 61(3), 756–764. https://doi.org/10.1109/TBME.2013.2289306 Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, 565–571. https://doi.org/10.48550/arxiv.1606.04797 MONAI. (s.f.) Retrieved March 7, 2022, from https://monai.io/ Oktay, O., Schlemper, J., Folgoc, L. le, Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning Where to Look for the Pancreas. https://doi.org/10.48550/arxiv.1804.03999 Pasquier, D., Lacornerie, T., Vermandel, M., Rousseau, J., Lartigau, E., & Betrouni, N. (2007). Automatic Segmentation of Pelvic Structures From Magnetic Resonance Images for Prostate Cancer Radiotherapy. International Journal of Radiation Oncology, Biology, Physics, 68(2), 592–600. https://doi.org/10.1016/J.IJROBP.2007.02.005 Qin, X. (2019). Transfer Learning with Edge Attention for Prostate MRI Segmentation. https://doi.org/10.48550/arXiv.1912.09847 Qian, Y. (2021). ProsegNet: A new network of prostate segmentation based on MR images. IEEE Access, 9, 106293–106302. https://doi.org/10.1109/ACCESS.2021.3096665 Qiu, W., Yuan, J., Ukwatta, E., Sun, Y., Rajchl, M., & Fenster, A. (2014). Dual optimization based prostate zonal segmentation in 3D MR images. Medical Image Analysis, 18(4), 660–673. https://doi.org/10.1016/J.MEDIA.2014.02.009 Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9351, 234–241. https://doi.org/10.48550/arxiv.1505.04597 Rundo, L., Militello, C., Russo, G., Garufi, A., Vitabile, S., Gilardi, M. C., & Mauri, G. (2017). Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging. Information 2017, Vol. 8, Page 49, 8(2), 49. https://doi.org/10.3390/INFO8020049 Shelhamer, E., Long, J., & Darrell, T. (2014). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640–651. https://doi.org/10.1109/TPAMI.2016.2572683 The Global Cancer Observatory (GCO). (2021). Colombia. https://gco.iarc.fr/today/data/factsheets/populations/170-colombia-fact-sheets.pdf Toth, R., & Madabhushi, A. (2012). Multifeature landmark-free active appearance models: application to prostate MRI segmentation. IEEE Transactions on Medical Imaging, 31(8), 1638–1650. https://doi.org/10.1109/TMI.2012.2201498 Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310–1320. https://doi.org/10.1109/TMI.2010.2046908 World Cancer Research Fund & American Cancer Society. (2018). Diet, nutrition, physical activity and prostate cancer. https://www.wcrf.org/wp-content/uploads/2021/02/prostate-cancer-report.pdf Yu, F., Koltun, V., & Funkhouser, T. (2017). Dilated Residual Networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 636–644. https://doi.org/10.48550/arxiv.1705.09914 Yu, L., Yang, X., Chen, H., Qin, J., & Heng, P. A. (2017). Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d MR images. 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 66–72. Zhou, W., Tao, X., Wei, Z., & Lin, L. (2020). Automatic segmentation of 3D prostate MR images with iterative localization refinement. Digital Signal Processing, 98, 102649. https://doi.org/10.1016/J.DSP.2019.102649 Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11045 LNCS, 3–11. https://doi.org/10.48550/arxiv.1807.10165
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.department.spa.fl_str_mv	Departamento de la Computación y la Decisión
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Branch Bedoya, John Willian7e38ec86da58a9547c188086b39efee8600Ospina Arango, Juan Davidfcac13e6ecb40f5f46d7d8439e931de7600Duque Miranda, Juan Esteban287b88fd90c1fbe258e036a9e984a2d0Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial2022-03-10T15:19:59Z2022-03-10T15:19:59Z2021-12-09https://repositorio.unal.edu.co/handle/unal/81179Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasLa segmentación de la próstata en imágenes de resonancia magnética se considera una tarea esencial para la planificación quirúrgica, así como la determinación de los estadios de enfermedades como el cáncer de próstata y la hiperplasia prostática benigna. Sin embargo, la falta de estandarización en los protocolos de adquisición de las imágenes y la heterogeneidad entre individuos hacen que esta sea una tarea difícil. Con el fin de aportar a la solución de este problema, se propone una arquitectura de red convolucional en forma de 3D Unet, que se caracteriza por tener una mayor profundidad, además de tener una fase codificación – decodificación con una compuerta de atención. Esta propuesta a diferencia de otras implementa un bloque residual similar al de la Resnet101 con una normalización por lotes. Además, utiliza una función de pérdida compuesta por el coeficiente de Dice y la entropía cruzada para manejar el problema de desequilibrio de clase. Durante la etapa de inferencia cada imagen es dividida en imágenes más pequeñas y se generan predicciones individuales, finalmente estas se unen para generar una máscara de predicción del mismo tamaño de la imagen de entrada. Para evaluar la arquitectura se utilizaron los datos del PROMISE12. Los resultados muestran desempeño superior o similar a otros métodos propuestos en la literatura. (Texto tomado de la fuente)Prostate segmentation on magnetic resonance imaging is considered an essential task for surgical planning as well as staging of diseases such as prostate cancer and benign prostatic hyperplasia. However, the lack of standardization in image acquisition protocols and the heterogeneity between individuals make this a difficult task. To contribute to the solution of this problem, a convolutional network architecture in the form of 3D Unet is proposed, which is characterized by having greater depth, in addition to having an encoding-decoding phase with an attention gate. Unlike others, this proposal implements a residual block similar to that of Resnet101 with batch normalization. Furthermore, it uses a loss function composed of the Dice coefficient and the cross-entropy to handle the class imbalance problem. During the inference stage, each image is divided into smaller images and individual predictions are generated, finally these are joined to generate a prediction mask of the same size as the input image To evaluate the architecture, data from PROMISE12 were used. The results show superior or similar performance to other methods proposed in the literature.MaestríaMagíster en Ingeniería – Ingeniería de SistemasVisión por computadoraÁrea Curricular de Ingeniería de Sistemas e Informáticaxi, 29 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Ingeniería de SistemasDepartamento de la Computación y la DecisiónFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales610 - Medicina y saludInteligencia artificialArtificial intelligenceTecnología médicaMedical technologySegmentación de la próstataResonancia magnéticaCompuerta de atenciónProstate segmentationmagnetic resonanceUnetattention gateResnetMétodo de segmentación de imágenes de la próstata tomadas mediante resonancia magnética mediante técnicas de inteligencia artificialSegmentation of the prostate in magnetic resonance images using artificial intelligence techniquesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAldoj, N., Biavati, F., Michallek, F., Stober, S., & Dewey, M. (2020). Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net. Scientific Reports 2020 10:1, 10(1), 1–17. https://doi.org/10.1038/s41598-020-71080-0American Cancer Society (ACS). (s.f.). Key Statistics for Prostate Cancer. https://www.cancer.org/cancer/prostate-cancer/about/key-statistics.html#written_byBravo, L. E., & Muñoz, N. (2018). Epidemiology of cancer in Colombia. Colombia Médica, 49(1), 9–12. https://doi.org/10.25100/CM.V49I1.3877Chen, S., Ma, K., & Zheng, Y. (2019). Med3D: Transfer Learning for 3D Medical Image Analysis. https://doi.org/10.48550/arxiv.1904.00625Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9901 LNCS, 424–432. https://doi.org/10.48550/arxiv.1606.06650Comelli, A., Dahiya, N., Stefano, A., Vernuccio, F., Portoghese, M., Cutaia, G., Bruno, A., Salvaggio, G., & Yezzi, A. (2021). Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging. Applied Sciences 2021, Vol. 11, Page 782, 11(2), 782. https://doi.org/10.3390/APP11020782Boor, C. (1972). On calculating with B-splines. Journal of Approximation Theory, 6(1), 50–62. https://doi.org/10.1016/0021-9045(72)90080-9Ghavami, N., Hu, Y., Gibson, E., Bonmati, E., Emberton, M., Moore, C. M., & Barratt, D. C. (2019). Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration. Medical Image Analysis, 58, 101558. https://doi.org/10.1016/J.MEDIA.2019.101558Guo, Y., Gao, Y., & Shen, D. (2016). Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching. IEEE Transactions on Medical Imaging, 35(4), 1077–1089. https://doi.org/10.1109/TMI.2015.2508280He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-December, 770–778. https://doi.org/10.48550/arxiv.1512.03385Isensee, F., Jäger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2020). Automated Design of Deep Learning Methods for Biomedical Image Segmentation. Nature Methods, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-zJia, H., Song, Y., Huang, H., Cai, W., & Xia, Y. (2019). HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11765 LNCS, 110–118. https://doi.org/10.1007/978-3-030-32245-8_13Jia, H., Xia, Y., Song, Y., Zhang, D., Huang, H., Zhang, Y., & Cai, W. (2019). 3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images. IEEE Transactions on Medical Imaging, 39(2), 447–457. https://doi.org/10.1109/TMI.2019.2928056Khan, Z., Yahya, N., Alsaih, K., Al-Hiyali, M. I., & Meriaudeau, F. (2021). Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review. IEEE Access, 9, 97878–97905. https://doi.org/10.1109/ACCESS.2021.3090825Klein, S., van der Heide, U. A., Lips, I. M., van Vulpen, M., Staring, M., & Pluim, J. P. W. (2008). Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics, 35(4), 1407–1417. https://doi.org/10.1118/1.2842076Larsen, C. T., Eugenio Iglesias, J., & van Leemput, K. (2014). N3 bias field correction explained as a Bayesian modeling method. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8677, 1–12. https://doi.org/10.1007/978-3-319-12289-2_1Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., Strand, R., Malmberg, F., Ou, Y., Davatzikos, C., Kirschner, M., Jung, F., Yuan, J., Qiu, W., Gao, Q., … Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2), 359–373. https://doi.org/10.1016/J.MEDIA.2013.12.002Mahapatra, D., & Buhmann, J. M. (2014). Prostate MRI segmentation using learned semantic knowledge and graph cuts. IEEE Transactions on Biomedical Engineering, 61(3), 756–764. https://doi.org/10.1109/TBME.2013.2289306Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings - 2016 4th International Conference on 3D Vision, 3DV 2016, 565–571. https://doi.org/10.48550/arxiv.1606.04797MONAI. (s.f.) Retrieved March 7, 2022, from https://monai.io/Oktay, O., Schlemper, J., Folgoc, L. le, Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention U-Net: Learning Where to Look for the Pancreas. https://doi.org/10.48550/arxiv.1804.03999Pasquier, D., Lacornerie, T., Vermandel, M., Rousseau, J., Lartigau, E., & Betrouni, N. (2007). Automatic Segmentation of Pelvic Structures From Magnetic Resonance Images for Prostate Cancer Radiotherapy. International Journal of Radiation Oncology, Biology, Physics, 68(2), 592–600. https://doi.org/10.1016/J.IJROBP.2007.02.005Qin, X. (2019). Transfer Learning with Edge Attention for Prostate MRI Segmentation. https://doi.org/10.48550/arXiv.1912.09847Qian, Y. 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Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11045 LNCS, 3–11. https://doi.org/10.48550/arxiv.1807.10165InvestigadoresORIGINAL1020445136.2021.pdf1020445136.2021.pdfTesis de Maestría en Ingeniería - Ingeniería de Sistemasapplication/pdf1104174https://repositorio.unal.edu.co/bitstream/unal/81179/3/1020445136.2021.pdf5a06c8b48257b5bed8295ce12704facfMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81179/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL1020445136.2021.pdf.jpg1020445136.2021.pdf.jpgGenerated Thumbnailimage/jpeg5296https://repositorio.unal.edu.co/bitstream/unal/81179/4/1020445136.2021.pdf.jpgac72db5322c9151e425b9d2a20669501MD54unal/81179oai:repositorio.unal.edu.co:unal/811792023-10-04 12:15:21.368Repositorio Institucional Universidad Nacional de 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

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