Development of surface correction software for medical image segmentation

ARDS (Acute respiratory distress syndrome) is a severe and life-threatening respiratory syn- drome, characterized by lung inflammation, infiltration, alveolar edema and progressive hypox- emia. It is not the result of a specific condition, but rather a complication that can arise from existing condi...

Full description

Autores:
Garzón Robayo, Pablo Andrés
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2023
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/69227
Acceso en línea:
http://hdl.handle.net/1992/69227
Palabra clave:
Medical imaging
3d meshes
ARDS
3d surfaces
Medical segmentation correction
Medical software
Ingeniería
Rights
openAccess
License
https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
id UNIANDES2_f2e88ce1123571c96fd74a2506599d7f
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network_name_str Séneca: repositorio Uniandes
repository_id_str
dc.title.none.fl_str_mv Development of surface correction software for medical image segmentation
title Development of surface correction software for medical image segmentation
spellingShingle Development of surface correction software for medical image segmentation
Medical imaging
3d meshes
ARDS
3d surfaces
Medical segmentation correction
Medical software
Ingeniería
title_short Development of surface correction software for medical image segmentation
title_full Development of surface correction software for medical image segmentation
title_fullStr Development of surface correction software for medical image segmentation
title_full_unstemmed Development of surface correction software for medical image segmentation
title_sort Development of surface correction software for medical image segmentation
dc.creator.fl_str_mv Garzón Robayo, Pablo Andrés
dc.contributor.advisor.none.fl_str_mv Orkisz, Maciej
Dávila Serrano, Eduardo Enrique
Hernández Hoyos, Marcela
dc.contributor.author.none.fl_str_mv Garzón Robayo, Pablo Andrés
dc.subject.keyword.none.fl_str_mv Medical imaging
3d meshes
ARDS
3d surfaces
Medical segmentation correction
Medical software
topic Medical imaging
3d meshes
ARDS
3d surfaces
Medical segmentation correction
Medical software
Ingeniería
dc.subject.themes.es_CO.fl_str_mv Ingeniería
description ARDS (Acute respiratory distress syndrome) is a severe and life-threatening respiratory syn- drome, characterized by lung inflammation, infiltration, alveolar edema and progressive hypox- emia. It is not the result of a specific condition, but rather a complication that can arise from existing conditions or as a result of infection and injuries. In most cases, patients require exten- sive care and mechanical breathing until the underlying condition is treated and the patient can use their lungs correctly. An essential step in ARDS treatment is the segmentation of CT(computerized tomography) images used to assess the lung conditions. However, such activity can be very time consuming if done manually, therefore, there is a lot of work being done to develop AI(Artificial Intelligence) automated methods that can provide rapid and accurate segmentations. Although the use of AI Has shown promising accuracy, errors can present themselves in some cases. Consequently, the development of such methods requires a mechanism for correcting erroneous segmentations, so that specialists can use them immediately on patients and to facilitate retraining of the existing AI models. Subsequently, this work focuses on the development and improvement of correction tools for resulting lung segmentations created by AI. The tools take advantage of the 3D mask created from the AI, utilize it as a 3D surface and operate directly on the mesh representing it, something which is not usually done on segmentation editing software. This way, the developed tools integrate numerous mesh editing techniques focused on the modification of a 3D mesh representing a lung surface. The development was done on the existing creaSDRA medical software which integrates multiple modules used in the segmentation process. Ultimately, the majority of the existing tools underwent substantial modifications to align with the specified requirements, augment their functionality, and optimize their usability. This comprehensive set of tools encompassed a deformation tool, a smoothing tool, and two surface- based tools. Moreover, in order to enhance the overall functionality and user experience of the software, two supplementary tools were developed, accompanied by multiple user interface (UI) changes. These enhancements collectively aimed to improve the efficacy and usability of the software solution.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-08-04T18:20:11Z
dc.date.available.none.fl_str_mv 2023-08-04T18:20:11Z
dc.date.issued.none.fl_str_mv 2023-08-03
dc.type.es_CO.fl_str_mv Trabajo de grado - Pregrado
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dc.relation.references.es_CO.fl_str_mv Rhino3dmedical. https://rhino3dmedical.com/, 2022. Mirrakoi.
Key features of maya. https://www.autodesk.com/products/maya/features, 2023. Autodesk Inc
Rhinoceros 3d. https://www.rhino3d.com/, 2023. Robert McNeel & Associates.
The black box toolkit. https://www.creatis.insa-lyon.fr/site7/en/CreatoolsBBTK, n.d. CREATIS (Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé).
L. Ball, V. Vercesi, F. Costantino, K. Chandrapatham, and P. Pelosi. Lung imaging: How to get better look inside the lung. Annals of Translational Medicine, 5(14):294, 2017. doi: 10.21037/atm.2017.07.20. URL https://doi.org/10.21037/atm.2017.07.20.
G. Bellani, J. G. Laffey, T. Pham, E. Fan, L. Brochard, A. Esteban, L. Gattinoni, F. van Haren, A. Larsson, D. F. McAuley, M. Ranieri, G. Rubenfeld, B. T. Thompson, H. Wrigge, A. S. Slutsky, A. Pesenti, for the LUNG SAFE Investigators, and the ESICM Trials Group. Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA, 315(8):788-800, 02 2016. ISSN 0098-7484. doi: 10.1001/jama.2016.0291. URL https://doi.org/10.1001/jama.2016.0291.
L. Bitker, D. Talmor, and J.-C. Richard. Imaging the acute respiratory distress syndrome: Past, present and future. Intensive Care Medicine, 48(8):995-1008, 2022. doi: 10.1007/s00134-022-06809-8. URL https://doi.org/10.1007/s00134-022-06809-8.
M. Diamond, H. L. Peniston, D. K. Sanghavi, et al. Acute respiratory distress syndrome. In StatPearls. StatPearls Publishing, Treasure Island (FL), updated edition, April 6 2023. URL https://www.ncbi.nlm.nih.gov/books/NBK436002/.
T. A. D. T. Force*. Acute Respiratory Distress Syndrome: The Berlin Definition. JAMA, 307(23):2526-2533, 06 2012. ISSN 0098-7484. doi: 10.1001/jama.2012.5669. URL https://doi.org/10.1001/jama.2012.5669.
P. G. Gibson, L. Qin, and S. H. Puah. Covid-19 acute respiratory distress syndrome (ards): Clinical features and differences from typical pre-covid-19 ards. The Medical Journal of Australia, 213(2):54-56.e1, 2020. doi: 10.5694/mja2.50674. URL https://doi.org/10.5694/mja2.50674.
H. Johnson, M. McCormick, and L. Ibanez. The ITK Software Guide: Design and Functionality. Kitware Inc., fourth edition, 2015. ISBN 978-1-930934-28
R. Kimmel and J. A. Sethian. Computing geodesic paths on manifolds. Proceedings of the National Academy of Sciences, 95(15):8431-8435, 1998. doi: 10.1073/pnas.95.15.8431. URL https://www.pnas.org/doi/abs/10.1073/pnas.95.15.8431.
R. Marshall, G. Bellingan, and G. Laurent. The acute respiratory distress syndrome: Fibrosis in the fast lane. Thorax, 53(9):815-817, 1998
N. J. Meyer, L. Gattinoni, and C. S. Calfee. Acute respiratory distress syndrome. The Lancet, 398(10300):622-637, 2021. ISSN 0140-6736. doi: https://doi.org/10.1016/S0140-6736(21)00439-6. URL https://www.sciencedirect.com/science/article/pii/S0140673621004396.
L. Penarrubia, N. Pinon, E. Roux, E. Dávila Serrano, J.-C. Richard, M. Orkisz, and D. Sar rut. Improving motion-mask segmentation in thoracic CT with multiplanar U-nets. Med ical Physics, 49(1):420-431, 2022. doi: 10.1002/mp.15347. URL https://hal.science/hal-03464276.
C. Pinter, A. Lasso, and G. Fichtinger. Polymorph segmentation representation for medical image computing. Computer Methods and Programs in Biomedicine, 171:19-26, 2019. ISSN 0169-2607. doi: https://doi.org/10.1016/j.cmpb.2019.02.011. URL https://www.sciencedirect.com/science/article/pii/S0169260718313038.
G. Rawal, S. Yadav, and R. Kumar. Acute respiratory distress syndrome: An update and review. Journal of translational internal medicine, 6(2):74-77, 2018. doi: 10.1515/jtim-2016-0012. URL https://doi.org/10.1515/jtim-2016-0012.
W. Schroeder, K. Martin, and B. Lorensen. The Visualization Toolkit (4th ed.). Kitware, 2006. ISBN 978-1-930934-19-1.
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spelling Orkisz, Maciej5dfd6039-2e82-4a23-833e-619a1e93a10a600Dávila Serrano, Eduardo Enrique1d79e72c-77f9-4752-8592-055a6a38de69600Hernández Hoyos, Marcelavirtual::14852-1Garzón Robayo, Pablo Andrésd3d9b5f6-0ce5-49f1-aea7-2143957385c96002023-08-04T18:20:11Z2023-08-04T18:20:11Z2023-08-03http://hdl.handle.net/1992/69227instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/ARDS (Acute respiratory distress syndrome) is a severe and life-threatening respiratory syn- drome, characterized by lung inflammation, infiltration, alveolar edema and progressive hypox- emia. It is not the result of a specific condition, but rather a complication that can arise from existing conditions or as a result of infection and injuries. In most cases, patients require exten- sive care and mechanical breathing until the underlying condition is treated and the patient can use their lungs correctly. An essential step in ARDS treatment is the segmentation of CT(computerized tomography) images used to assess the lung conditions. However, such activity can be very time consuming if done manually, therefore, there is a lot of work being done to develop AI(Artificial Intelligence) automated methods that can provide rapid and accurate segmentations. Although the use of AI Has shown promising accuracy, errors can present themselves in some cases. Consequently, the development of such methods requires a mechanism for correcting erroneous segmentations, so that specialists can use them immediately on patients and to facilitate retraining of the existing AI models. Subsequently, this work focuses on the development and improvement of correction tools for resulting lung segmentations created by AI. The tools take advantage of the 3D mask created from the AI, utilize it as a 3D surface and operate directly on the mesh representing it, something which is not usually done on segmentation editing software. This way, the developed tools integrate numerous mesh editing techniques focused on the modification of a 3D mesh representing a lung surface. The development was done on the existing creaSDRA medical software which integrates multiple modules used in the segmentation process. Ultimately, the majority of the existing tools underwent substantial modifications to align with the specified requirements, augment their functionality, and optimize their usability. This comprehensive set of tools encompassed a deformation tool, a smoothing tool, and two surface- based tools. Moreover, in order to enhance the overall functionality and user experience of the software, two supplementary tools were developed, accompanied by multiple user interface (UI) changes. These enhancements collectively aimed to improve the efficacy and usability of the software solution.Ingeniero de Sistemas y ComputaciónPregrado57 páginasapplication/pdfengUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y Computaciónhttps://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Development of surface correction software for medical image segmentationTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPMedical imaging3d meshesARDS3d surfacesMedical segmentation correctionMedical softwareIngenieríaRhino3dmedical. https://rhino3dmedical.com/, 2022. Mirrakoi.Key features of maya. https://www.autodesk.com/products/maya/features, 2023. Autodesk IncRhinoceros 3d. https://www.rhino3d.com/, 2023. Robert McNeel & Associates.The black box toolkit. https://www.creatis.insa-lyon.fr/site7/en/CreatoolsBBTK, n.d. CREATIS (Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé).L. Ball, V. Vercesi, F. Costantino, K. Chandrapatham, and P. Pelosi. Lung imaging: How to get better look inside the lung. Annals of Translational Medicine, 5(14):294, 2017. doi: 10.21037/atm.2017.07.20. URL https://doi.org/10.21037/atm.2017.07.20.G. Bellani, J. G. Laffey, T. Pham, E. Fan, L. Brochard, A. Esteban, L. Gattinoni, F. van Haren, A. Larsson, D. F. McAuley, M. Ranieri, G. Rubenfeld, B. T. Thompson, H. Wrigge, A. S. Slutsky, A. Pesenti, for the LUNG SAFE Investigators, and the ESICM Trials Group. Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA, 315(8):788-800, 02 2016. ISSN 0098-7484. doi: 10.1001/jama.2016.0291. URL https://doi.org/10.1001/jama.2016.0291.L. Bitker, D. Talmor, and J.-C. Richard. Imaging the acute respiratory distress syndrome: Past, present and future. Intensive Care Medicine, 48(8):995-1008, 2022. doi: 10.1007/s00134-022-06809-8. URL https://doi.org/10.1007/s00134-022-06809-8.M. Diamond, H. L. Peniston, D. K. Sanghavi, et al. Acute respiratory distress syndrome. In StatPearls. StatPearls Publishing, Treasure Island (FL), updated edition, April 6 2023. URL https://www.ncbi.nlm.nih.gov/books/NBK436002/.T. A. D. T. Force*. Acute Respiratory Distress Syndrome: The Berlin Definition. JAMA, 307(23):2526-2533, 06 2012. ISSN 0098-7484. doi: 10.1001/jama.2012.5669. URL https://doi.org/10.1001/jama.2012.5669.P. G. Gibson, L. Qin, and S. H. Puah. Covid-19 acute respiratory distress syndrome (ards): Clinical features and differences from typical pre-covid-19 ards. The Medical Journal of Australia, 213(2):54-56.e1, 2020. doi: 10.5694/mja2.50674. URL https://doi.org/10.5694/mja2.50674.H. Johnson, M. McCormick, and L. Ibanez. The ITK Software Guide: Design and Functionality. Kitware Inc., fourth edition, 2015. ISBN 978-1-930934-28R. Kimmel and J. A. Sethian. Computing geodesic paths on manifolds. Proceedings of the National Academy of Sciences, 95(15):8431-8435, 1998. doi: 10.1073/pnas.95.15.8431. URL https://www.pnas.org/doi/abs/10.1073/pnas.95.15.8431.R. Marshall, G. Bellingan, and G. Laurent. The acute respiratory distress syndrome: Fibrosis in the fast lane. Thorax, 53(9):815-817, 1998N. J. Meyer, L. Gattinoni, and C. S. Calfee. Acute respiratory distress syndrome. The Lancet, 398(10300):622-637, 2021. ISSN 0140-6736. doi: https://doi.org/10.1016/S0140-6736(21)00439-6. URL https://www.sciencedirect.com/science/article/pii/S0140673621004396.L. Penarrubia, N. Pinon, E. Roux, E. Dávila Serrano, J.-C. Richard, M. Orkisz, and D. Sar rut. Improving motion-mask segmentation in thoracic CT with multiplanar U-nets. Med ical Physics, 49(1):420-431, 2022. doi: 10.1002/mp.15347. URL https://hal.science/hal-03464276.C. Pinter, A. Lasso, and G. Fichtinger. Polymorph segmentation representation for medical image computing. Computer Methods and Programs in Biomedicine, 171:19-26, 2019. ISSN 0169-2607. doi: https://doi.org/10.1016/j.cmpb.2019.02.011. URL https://www.sciencedirect.com/science/article/pii/S0169260718313038.G. Rawal, S. Yadav, and R. Kumar. Acute respiratory distress syndrome: An update and review. Journal of translational internal medicine, 6(2):74-77, 2018. doi: 10.1515/jtim-2016-0012. URL https://doi.org/10.1515/jtim-2016-0012.W. Schroeder, K. Martin, and B. Lorensen. The Visualization Toolkit (4th ed.). Kitware, 2006. 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