Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images
Glioblastoma is the most common and aggressive glioma in adults. Its complexity demands the development of methods able to maximize the capture of personalized information to let the design of patient-specific therapies, which can be achieved through radiomics studies. In this process, initial segme...
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2018
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815386_v_n_p_DeLosReyes http://hdl.handle.net/20.500.12110/paper_97815386_v_n_p_DeLosReyes |
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paper:paper_97815386_v_n_p_DeLosReyes2023-06-08T16:38:02Z Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images Gliomas Image segmentation Magnetic resonance images K-means clustering Magnetic resonance Magnetic resonance imaging Medical imaging Tumors Adaptive thresholds Gliomas Graphic interfaces Initial segmentation Personalized information Segmented regions Similarity coefficients Three-dimensional resolution Image segmentation Glioblastoma is the most common and aggressive glioma in adults. Its complexity demands the development of methods able to maximize the capture of personalized information to let the design of patient-specific therapies, which can be achieved through radiomics studies. In this process, initial segmentation of the image is fundamental. In glioblastoma, the resulting segmented region of interest (ROI) must include the active tumor, its inner necrosis and the peripheral edema, a zone estimated to be infiltrated by tumor cells. In a first step, images corresponding to the different modalities of the MRI were registered to achieve spatial coincidence and the same three-dimensional resolution. In a second step the whole brain were segmented based on T1 images, to eliminate not-nervous tissues. Then the complete ROI region were determined through on a combination of FLAIR and T2 modalities and, finally, inner ROI components were defined working with the contrasted T1 modality. During these processes, K-means clustering, Chan-Vese active contours, adaptive thresholds, dilatation, erosion and replenishment algorithms were developed and grouped in the Matlab graphic interface RMIanalizer to interact with the user and visualize results. This interface can upload any format of medical image, segmentate semiautomatic and three-dimensionally the ROI components, and determine the estimated volume of each one. Preliminary results were compared with the »ground truth» cases submitted by the web database used, obtaining a Dice similarity coefficient of 0.886 +/- 0.0386 for the complete ROI region, over a total of 10 glioblastoma cases. © 2018 IEEE. 2018 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815386_v_n_p_DeLosReyes http://hdl.handle.net/20.500.12110/paper_97815386_v_n_p_DeLosReyes |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Gliomas Image segmentation Magnetic resonance images K-means clustering Magnetic resonance Magnetic resonance imaging Medical imaging Tumors Adaptive thresholds Gliomas Graphic interfaces Initial segmentation Personalized information Segmented regions Similarity coefficients Three-dimensional resolution Image segmentation |
| spellingShingle |
Gliomas Image segmentation Magnetic resonance images K-means clustering Magnetic resonance Magnetic resonance imaging Medical imaging Tumors Adaptive thresholds Gliomas Graphic interfaces Initial segmentation Personalized information Segmented regions Similarity coefficients Three-dimensional resolution Image segmentation Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images |
| topic_facet |
Gliomas Image segmentation Magnetic resonance images K-means clustering Magnetic resonance Magnetic resonance imaging Medical imaging Tumors Adaptive thresholds Gliomas Graphic interfaces Initial segmentation Personalized information Segmented regions Similarity coefficients Three-dimensional resolution Image segmentation |
| description |
Glioblastoma is the most common and aggressive glioma in adults. Its complexity demands the development of methods able to maximize the capture of personalized information to let the design of patient-specific therapies, which can be achieved through radiomics studies. In this process, initial segmentation of the image is fundamental. In glioblastoma, the resulting segmented region of interest (ROI) must include the active tumor, its inner necrosis and the peripheral edema, a zone estimated to be infiltrated by tumor cells. In a first step, images corresponding to the different modalities of the MRI were registered to achieve spatial coincidence and the same three-dimensional resolution. In a second step the whole brain were segmented based on T1 images, to eliminate not-nervous tissues. Then the complete ROI region were determined through on a combination of FLAIR and T2 modalities and, finally, inner ROI components were defined working with the contrasted T1 modality. During these processes, K-means clustering, Chan-Vese active contours, adaptive thresholds, dilatation, erosion and replenishment algorithms were developed and grouped in the Matlab graphic interface RMIanalizer to interact with the user and visualize results. This interface can upload any format of medical image, segmentate semiautomatic and three-dimensionally the ROI components, and determine the estimated volume of each one. Preliminary results were compared with the »ground truth» cases submitted by the web database used, obtaining a Dice similarity coefficient of 0.886 +/- 0.0386 for the complete ROI region, over a total of 10 glioblastoma cases. © 2018 IEEE. |
| title |
Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images |
| title_short |
Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images |
| title_full |
Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images |
| title_fullStr |
Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images |
| title_full_unstemmed |
Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images |
| title_sort |
development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images |
| publishDate |
2018 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97815386_v_n_p_DeLosReyes http://hdl.handle.net/20.500.12110/paper_97815386_v_n_p_DeLosReyes |
| _version_ |
1768545809623678976 |