Arquitectura profunda en cascada para la segmentación de lesiones de accidente cerebrovascular y la generación de mapas paramétricos sintéticos sobre estudios de TC
Autores/as
Santiago Gomez
Perfil Google Scholar
Julian Garcia
Fabio Martínez Carrillo
Ver/Descargar
PDF (English)
FLIP
Cómo citar
Florez, S., Gomez, S., Garcia, J., & Martínez Carrillo, F. (2024). Arquitectura profunda en cascada para la segmentación de lesiones de accidente cerebrovascular y la generación de mapas paramétricos sintéticos sobre estudios de TC. International Journal of Psychological Research, 17(2), 47–53. https://doi.org/10.21500/20112084.7013
Más formatos de cita
Términos de licencia
▼
The work that is sent to this journal must be original, not published or sent to be published elsewhere; and if it is accepted for publication, authors will agree to transfer copyright to International Journal of Psychological Research.
To give up copyright, the authors allow that, International Journal of Psychological Research, distribute the work more broadly, check for the reuse by others and take care of the necessary procedures for the registration and administration of copyright; at the same time, our editorial board represents the interests of the author and allows authors to re-use his work in various forms. In response to the above, authors transfer copyright to the journal, International Journal of Psychological Research. This transfer does not imply other rights which are not those of authorship (for example those that concern about patents). Likewise, preserves the authors rights to use the work integral or partially in lectures, books and courses, as well as make copies for educational purposes. Finally, the authors may use freely the tables and figures in its future work, wherever make explicit reference to the previous publication in International Journal of Psychological Research. The assignment of copyright includes both virtual rights and forms of the article to allow the editorial to disseminate the work in the manner which it deems appropriate.
The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
To give up copyright, the authors allow that, International Journal of Psychological Research, distribute the work more broadly, check for the reuse by others and take care of the necessary procedures for the registration and administration of copyright; at the same time, our editorial board represents the interests of the author and allows authors to re-use his work in various forms. In response to the above, authors transfer copyright to the journal, International Journal of Psychological Research. This transfer does not imply other rights which are not those of authorship (for example those that concern about patents). Likewise, preserves the authors rights to use the work integral or partially in lectures, books and courses, as well as make copies for educational purposes. Finally, the authors may use freely the tables and figures in its future work, wherever make explicit reference to the previous publication in International Journal of Psychological Research. The assignment of copyright includes both virtual rights and forms of the article to allow the editorial to disseminate the work in the manner which it deems appropriate.
The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
Resumen
El accidente cerebrovascular (ACV), segunda causa de muerte en el mundo, requiere un diagnóstico temprano para un pronóstico favorable.
Las imágenes de TC tienen limitaciones, especialmente en la identificación de lesiones agudas. Este trabajo introduce una novedosa representación profunda que utiliza datos multimodales TC y mapas paramétricos de
perfusión para segmentar lesiones de ACV. La arquitectura sigue una representación autocodificadora que fuerza la atención sobre la geometría
del ACV a través de módulos aditivos de atención cruzada. Además, se propone un entrenamiento en cascada para generar mapas de perfusión
sintéticos que complementen las entradas multimodales, refinando la segmentación de las lesiones en cada etapa del procesamiento y apoyando
el análisis observacional del experto. El enfoque propuesto fue validado en el conjunto de datos ISLES 2018 con 92 estudios; el método supera a
las técnicas clásicas con una puntuación Dice de .66 y una precisión de .67.
Palabras clave:
Referencias
Clerigues, A., Valverde, S., Bernal, J., Freixenet, J., Oliver, A., & Lladó, X. (2019). Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Computers in Biology and Medicine, 115, 103487.
Dolz, J., Ayed, I. B., & Desrosiers, C. (2018). Dense multi-path U-Net for ischemic stroke lesion segmentation in multiple image modalities. International MICCAI Brainlesion Workshop, 271–282.
Gómez, S., Mantilla, D., Rangel, E., Ortiz, A., D Vera, D., & Martínez, F. (2023). A deep supervised cross-attention strategy for ischemic stroke segmentation in MRI studies. Biomedical Physics & Engineering Express, 9(3), 035026. https://doi.org/10.1088/2057-1976/acc853
Li, R., Wang, X., Huang, G., Yang, W., Zhang, K., Gu, X., Tran, S. N., Garg, S., Alty, J., & Bai, Q. (2022). A comprehensive review on deep supervision: Theories and applications. arXiv Preprint arXiv:2207.02376.
Liu, L., Kurgan, L., Wu, F. X., & Wang, J. (2020). Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Medical Image Analysis, 65. https://doi.org/10.1016/j.media.2020.101791
Liu, P. (2019). Stroke lesion segmentation with 2D novel CNN pipeline and novel loss function. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, 253–262.
Martel, A. L., Allder, S. J., Delay, G. S., Morgan, P. S., & Moody, A. R. (1999). Measurement of infarct volume in stroke patients using adaptive segmentation of diffusion weighted MR images. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/10704282_3
Neumann, A. B., Jonsdottir, K. Y., Mouridsen, K., Hjort, N., Gyldensted, C., Bizzi, A., Fiehler, J., Gasparotti, R., Gillard, J. H., Hermier, M., Kucinski, T., Larsson, E. M., Sørensen, L., & Østergaard, L. (2009). Interrater agreement for final infarct mri lesion delineation. Stroke, 40(12), 3768–3771. https://doi.org/10.1161/STROKEAHA.108.545368
Rekik, I., Allassonnière, S., Carpenter, T. K., & Wardlaw, J. M. (2012). Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal. NeuroImage: Clinical, 1(1), 164–178.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, 234–241.
Roth, G. A., Mensah, G. A., Johnson, C. O., Addolorato, G., Ammirati, E., Baddour, L. M., Barengo, N. C., Beaton, A. Z., Benjamin, E. J., Benziger, C. P., & others. (2020). Global burden of cardiovascular diseases and risk factors, 1990–2019: Update from the GBD 2019 study. Journal of the American College of Cardiology, 76(25), 2982–3021.
Tureckova, A., & Rodríguez-Sánchez, A. J. (2018). ISLES challenge: U-shaped convolution neural network with dilated convolution for 3D stroke lesion segmentation. International MICCAI Brainlesion Workshop, 319–327.
Von Kummer, R., Meyding-Lamade, U., Forsting, M., Rosin, L., Rieke, K., Hacke, W., Sartor, K., & Tomsick, T. A. (1994). Sensitivity and prognostic value of early CT in occlusion of the middle cerebral artery trunk. American Journal of Neuroradiology, 15(1), 9–18.
Wang, G., Song, T., Dong, Q., Cui, M., Huang, N., & Zhang, S. (2020). Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Medical Image Analysis, 65, 101787. https://doi.org/10.1016/j.media.2020.101787
Dolz, J., Ayed, I. B., & Desrosiers, C. (2018). Dense multi-path U-Net for ischemic stroke lesion segmentation in multiple image modalities. International MICCAI Brainlesion Workshop, 271–282.
Gómez, S., Mantilla, D., Rangel, E., Ortiz, A., D Vera, D., & Martínez, F. (2023). A deep supervised cross-attention strategy for ischemic stroke segmentation in MRI studies. Biomedical Physics & Engineering Express, 9(3), 035026. https://doi.org/10.1088/2057-1976/acc853
Li, R., Wang, X., Huang, G., Yang, W., Zhang, K., Gu, X., Tran, S. N., Garg, S., Alty, J., & Bai, Q. (2022). A comprehensive review on deep supervision: Theories and applications. arXiv Preprint arXiv:2207.02376.
Liu, L., Kurgan, L., Wu, F. X., & Wang, J. (2020). Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Medical Image Analysis, 65. https://doi.org/10.1016/j.media.2020.101791
Liu, P. (2019). Stroke lesion segmentation with 2D novel CNN pipeline and novel loss function. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, 253–262.
Martel, A. L., Allder, S. J., Delay, G. S., Morgan, P. S., & Moody, A. R. (1999). Measurement of infarct volume in stroke patients using adaptive segmentation of diffusion weighted MR images. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/10704282_3
Neumann, A. B., Jonsdottir, K. Y., Mouridsen, K., Hjort, N., Gyldensted, C., Bizzi, A., Fiehler, J., Gasparotti, R., Gillard, J. H., Hermier, M., Kucinski, T., Larsson, E. M., Sørensen, L., & Østergaard, L. (2009). Interrater agreement for final infarct mri lesion delineation. Stroke, 40(12), 3768–3771. https://doi.org/10.1161/STROKEAHA.108.545368
Rekik, I., Allassonnière, S., Carpenter, T. K., & Wardlaw, J. M. (2012). Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal. NeuroImage: Clinical, 1(1), 164–178.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, 234–241.
Roth, G. A., Mensah, G. A., Johnson, C. O., Addolorato, G., Ammirati, E., Baddour, L. M., Barengo, N. C., Beaton, A. Z., Benjamin, E. J., Benziger, C. P., & others. (2020). Global burden of cardiovascular diseases and risk factors, 1990–2019: Update from the GBD 2019 study. Journal of the American College of Cardiology, 76(25), 2982–3021.
Tureckova, A., & Rodríguez-Sánchez, A. J. (2018). ISLES challenge: U-shaped convolution neural network with dilated convolution for 3D stroke lesion segmentation. International MICCAI Brainlesion Workshop, 319–327.
Von Kummer, R., Meyding-Lamade, U., Forsting, M., Rosin, L., Rieke, K., Hacke, W., Sartor, K., & Tomsick, T. A. (1994). Sensitivity and prognostic value of early CT in occlusion of the middle cerebral artery trunk. American Journal of Neuroradiology, 15(1), 9–18.
Wang, G., Song, T., Dong, Q., Cui, M., Huang, N., & Zhang, S. (2020). Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Medical Image Analysis, 65, 101787. https://doi.org/10.1016/j.media.2020.101787
Descargas
Los datos de descargas todavía no están disponibles.
Citado por
